• Survey paper
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  • Published: 03 May 2022

A systematic review and research perspective on recommender systems

  • Deepjyoti Roy   ORCID: orcid.org/0000-0002-8020-7145 1 &
  • Mala Dutta 1  

Journal of Big Data volume  9 , Article number:  59 ( 2022 ) Cite this article

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Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system.


The recent advancements in technology along with the prevalence of online services has offered more abilities for accessing a huge amount of online information in a faster manner. Users can post reviews, comments, and ratings for various types of services and products available online. However, the recent advancements in pervasive computing have resulted in an online data overload problem. This data overload complicates the process of finding relevant and useful content over the internet. The recent establishment of several procedures having lower computational requirements can however guide users to the relevant content in a much easy and fast manner. Because of this, the development of recommender systems has recently gained significant attention. In general, recommender systems act as information filtering tools, offering users suitable and personalized content or information. Recommender systems primarily aim to reduce the user’s effort and time required for searching relevant information over the internet.

Nowadays, recommender systems are being increasingly used for a large number of applications such as web [ 1 , 67 , 70 ], books [ 2 ], e-learning [ 4 , 16 , 61 ], tourism [ 5 , 8 , 78 ], movies [ 66 ], music [ 79 ], e-commerce, news, specialized research resources [ 65 ], television programs [ 72 , 81 ], etc. It is therefore important to build high-quality and exclusive recommender systems for providing personalized recommendations to the users in various applications. Despite the various advances in recommender systems, the present generation of recommender systems requires further improvements to provide more efficient recommendations applicable to a broader range of applications. More investigation of the existing latest works on recommender systems is required which focus on diverse applications.

There is hardly any review paper that has categorically synthesized and reviewed the literature of all the classification fields and application domains of recommender systems. The few existing literature reviews in the field cover just a fraction of the articles or focus only on selected aspects such as system evaluation. Thus, they do not provide an overview of the application field, algorithmic categorization, or identify the most promising approaches. Also, review papers often neglect to analyze the dataset description and the simulation platforms used. This paper aims to fulfil this significant gap by reviewing and comparing existing articles on recommender systems based on a defined classification framework, their algorithmic categorization, simulation platforms used, applications focused, their features and challenges, dataset description and system performance. Finally, we provide researchers and practitioners with insight into the most promising directions for further investigation in the field of recommender systems under various applications.

In essence, recommender systems deal with two entities—users and items, where each user gives a rating (or preference value) to an item (or product). User ratings are generally collected by using implicit or explicit methods. Implicit ratings are collected indirectly from the user through the user’s interaction with the items. Explicit ratings, on the other hand, are given directly by the user by picking a value on some finite scale of points or labelled interval values. For example, a website may obtain implicit ratings for different items based on clickstream data or from the amount of time a user spends on a webpage and so on. Most recommender systems gather user ratings through both explicit and implicit methods. These feedbacks or ratings provided by the user are arranged in a user-item matrix called the utility matrix as presented in Table 1 .

The utility matrix often contains many missing values. The problem of recommender systems is mainly focused on finding the values which are missing in the utility matrix. This task is often difficult as the initial matrix is usually very sparse because users generally tend to rate only a small number of items. It may also be noted that we are interested in only the high user ratings because only such items would be suggested back to the users. The efficiency of a recommender system greatly depends on the type of algorithm used and the nature of the data source—which may be contextual, textual, visual etc.

Types of recommender systems

Recommender systems are broadly categorized into three different types viz. content-based recommender systems, collaborative recommender systems and hybrid recommender systems. A diagrammatic representation of the different types of recommender systems is given in Fig.  1 .

figure 1

Content-based recommender system

In content-based recommender systems, all the data items are collected into different item profiles based on their description or features. For example, in the case of a book, the features will be author, publisher, etc. In the case of a movie, the features will be the movie director, actor, etc. When a user gives a positive rating to an item, then the other items present in that item profile are aggregated together to build a user profile. This user profile combines all the item profiles, whose items are rated positively by the user. Items present in this user profile are then recommended to the user, as shown in Fig.  2 .

figure 2

One drawback of this approach is that it demands in-depth knowledge of the item features for an accurate recommendation. This knowledge or information may not be always available for all items. Also, this approach has limited capacity to expand on the users' existing choices or interests. However, this approach has many advantages. As user preferences tend to change with time, this approach has the quick capability of dynamically adapting itself to the changing user preferences. Since one user profile is specific only to that user, this algorithm does not require the profile details of any other users because they provide no influence in the recommendation process. This ensures the security and privacy of user data. If new items have sufficient description, content-based techniques can overcome the cold-start problem i.e., this technique can recommend an item even when that item has not been previously rated by any user. Content-based filtering approaches are more common in systems like personalized news recommender systems, publications, web pages recommender systems, etc.

Collaborative filtering-based recommender system

Collaborative approaches make use of the measure of similarity between users. This technique starts with finding a group or collection of user X whose preferences, likes, and dislikes are similar to that of user A. X is called the neighbourhood of A. The new items which are liked by most of the users in X are then recommended to user A. The efficiency of a collaborative algorithm depends on how accurately the algorithm can find the neighbourhood of the target user. Traditionally collaborative filtering-based systems suffer from the cold-start problem and privacy concerns as there is a need to share user data. However, collaborative filtering approaches do not require any knowledge of item features for generating a recommendation. Also, this approach can help to expand on the user’s existing interests by discovering new items. Collaborative approaches are again divided into two types: memory-based approaches and model-based approaches.

Memory-based collaborative approaches recommend new items by taking into consideration the preferences of its neighbourhood. They make use of the utility matrix directly for prediction. In this approach, the first step is to build a model. The model is equal to a function that takes the utility matrix as input.

Model = f (utility matrix)

Then recommendations are made based on a function that takes the model and user profile as input. Here we can make recommendations only to users whose user profile belongs to the utility matrix. Therefore, to make recommendations for a new user, the user profile must be added to the utility matrix, and the similarity matrix should be recomputed, which makes this technique computation heavy.

Recommendation = f (defined model, user profile) where user profile  ∈  utility matrix

Memory-based collaborative approaches are again sub-divided into two types: user-based collaborative filtering and item-based collaborative filtering. In the user-based approach, the user rating of a new item is calculated by finding other users from the user neighbourhood who has previously rated that same item. If a new item receives positive ratings from the user neighbourhood, the new item is recommended to the user. Figure  3 depicts the user-based filtering approach.

figure 3

User-based collaborative filtering

In the item-based approach, an item-neighbourhood is built consisting of all similar items which the user has rated previously. Then that user’s rating for a different new item is predicted by calculating the weighted average of all ratings present in a similar item-neighbourhood as shown in Fig.  4 .

figure 4

Item-based collaborative filtering

Model-based systems use various data mining and machine learning algorithms to develop a model for predicting the user’s rating for an unrated item. They do not rely on the complete dataset when recommendations are computed but extract features from the dataset to compute a model. Hence the name, model-based technique. These techniques also need two steps for prediction—the first step is to build the model, and the second step is to predict ratings using a function (f) which takes the model defined in the first step and the user profile as input.

Recommendation = f (defined model, user profile) where user profile  ∉  utility matrix

Model-based techniques do not require adding the user profile of a new user into the utility matrix before making predictions. We can make recommendations even to users that are not present in the model. Model-based systems are more efficient for group recommendations. They can quickly recommend a group of items by using the pre-trained model. The accuracy of this technique largely relies on the efficiency of the underlying learning algorithm used to create the model. Model-based techniques are capable of solving some traditional problems of recommender systems such as sparsity and scalability by employing dimensionality reduction techniques [ 86 ] and model learning techniques.

Hybrid filtering

A hybrid technique is an aggregation of two or more techniques employed together for addressing the limitations of individual recommender techniques. The incorporation of different techniques can be performed in various ways. A hybrid algorithm may incorporate the results achieved from separate techniques, or it can use content-based filtering in a collaborative method or use a collaborative filtering technique in a content-based method. This hybrid incorporation of different techniques generally results in increased performance and increased accuracy in many recommender applications. Some of the hybridization approaches are meta-level, feature-augmentation, feature-combination, mixed hybridization, cascade hybridization, switching hybridization and weighted hybridization [ 86 ]. Table 2 describes these approaches.

Recommender system challenges

This section briefly describes the various challenges present in current recommender systems and offers different solutions to overcome these challenges.

Cold start problem

The cold start problem appears when the recommender system cannot draw any inference from the existing data, which is insufficient. Cold start refers to a condition when the system cannot produce efficient recommendations for the cold (or new) users who have not rated any item or have rated a very few items. It generally arises when a new user enters the system or new items (or products) are inserted into the database. Some solutions to this problem are as follows: (a) Ask new users to explicitly mention their item preference. (b) Ask a new user to rate some items at the beginning. (c) Collect demographic information (or meta-data) from the user and recommend items accordingly.

Shilling attack problem

This problem arises when a malicious user fakes his identity and enters the system to give false item ratings [ 87 ]. Such a situation occurs when the malicious user wants to either increase or decrease some item’s popularity by causing a bias on selected target items. Shilling attacks greatly reduce the reliability of the system. One solution to this problem is to detect the attackers quickly and remove the fake ratings and fake user profiles from the system.

Synonymy problem

This problem arises when similar or related items have different entries or names, or when the same item is represented by two or more names in the system [ 78 ]. For example, babywear and baby cloth. Many recommender systems fail to distinguish these differences, hence reducing their recommendation accuracy. To alleviate this problem many methods are used such as demographic filtering, automatic term expansion and Singular Value Decomposition [ 76 ].

Latency problem

The latency problem is specific to collaborative filtering approaches and occurs when new items are frequently inserted into the database. This problem is characterized by the system’s failure to recommend new items. This happens because new items must be reviewed before they can be recommended in a collaborative filtering environment. Using content-based filtering may resolve this issue, but it may introduce overspecialization and decrease the computing time and system performance. To increase performance, the calculations can be done in an offline environment and clustering-based techniques can be used [ 76 ].

Sparsity problem

Data sparsity is a common problem in large scale data analysis, which arises when certain expected values are missing in the dataset. In the case of recommender systems, this situation occurs when the active users rate very few items. This reduces the recommendation accuracy. To alleviate this problem several techniques can be used such as demographic filtering, singular value decomposition and using model-based collaborative techniques.

Grey sheep problem

The grey sheep problem is specific to pure collaborative filtering approaches where the feedback given by one user do not match any user neighbourhood. In this situation, the system fails to accurately predict relevant items for that user. This problem can be resolved by using pure content-based approaches where predictions are made based on the user’s profile and item properties.

Scalability problem

Recommender systems, especially those employing collaborative filtering techniques, require large amounts of training data, which cause scalability problems. The scalability problem arises when the amount of data used as input to a recommender system increases quickly. In this era of big data, more and more items and users are rapidly getting added to the system and this problem is becoming common in recommender systems. Two common approaches used to solve the scalability problem is dimensionality reduction and using clustering-based techniques to find users in tiny clusters instead of the complete database.


The purpose of this study is to understand the research trends in the field of recommender systems. The nature of research in recommender systems is such that it is difficult to confine each paper to a specific discipline. This can be further understood by the fact that research papers on recommender systems are scattered across various journals such as computer science, management, marketing, information technology and information science. Hence, this literature review is conducted over a wide range of electronic journals and research databases such as ACM Portal, IEEE/IEE Library, Google Scholars and Science Direct [ 88 ].

The search process of online research articles was performed based on 6 descriptors: “Recommender systems”, “Recommendation systems”, “Movie Recommend*”, “Music Recommend*”, “Personalized Recommend*”, “Hybrid Recommend*”. The following research papers described below were excluded from our research:

News articles.

Master’s dissertations.

Non-English papers.

Unpublished papers.

Research papers published before 2011.

We have screened a total of 350 articles based on their abstracts and content. However, only research papers that described how recommender systems can be applied were chosen. Finally, 60 papers were selected from top international journals indexed in Scopus or E-SCI in 2021. We now present the PRISMA flowchart of the inclusion and exclusion process in Fig.  5 .

figure 5

PRISMA flowchart of the inclusion and exclusion process. Abstract and content not suitable to the study: * The use or application of the recommender system is not specified: **

Each paper was carefully reviewed and classified into 6 categories in the application fields and 3 categories in the techniques used to develop the system. The classification framework is presented in Fig.  6 .

figure 6

Classification framework

The number of relevant articles come from Expert Systems with Applications (23%), followed by IEEE (17%), Knowledge-Based System (17%) and Others (43%). Table 3 depicts the article distribution by journal title and Table 4 depicts the sector-wise article distribution.

Both forward and backward searching techniques were implemented to establish that the review of 60 chosen articles can represent the domain literature. Hence, this paper can demonstrate its validity and reliability as a literature review.

Review on state-of-the-art recommender systems

This section presents a state-of-art literature review followed by a chronological review of the various existing recommender systems.

Literature review

In 2011, Castellano et al. [ 1 ] developed a “NEuro-fuzzy WEb Recommendation (NEWER)” system for exploiting the possibility of combining computational intelligence and user preference for suggesting interesting web pages to the user in a dynamic environment. It considered a set of fuzzy rules to express the correlations between user relevance and categories of pages. Crespo et al. [ 2 ] presented a recommender system for distance education over internet. It aims to recommend e-books to students using data from user interaction. The system was developed using a collaborative approach and focused on solving the data overload problem in big digital content. Lin et al. [ 3 ] have put forward a recommender system for automatic vending machines using Genetic algorithm (GA), k-means, Decision Tree (DT) and Bayesian Network (BN). It aimed at recommending localized products by developing a hybrid model combining statistical methods, classification methods, clustering methods, and meta-heuristic methods. Wang and Wu [ 4 ] have implemented a ubiquitous learning system for providing personalized learning assistance to the learners by combining the recommendation algorithm with a context-aware technique. It employed the Association Rule Mining (ARM) technique and aimed to increase the effectiveness of the learner’s learning. García-Crespo et al. [ 5 ] presented a “semantic hotel” recommender system by considering the experiences of consumers using a fuzzy logic approach. The system considered both hotel and customer characteristics. Dong et al. [ 6 ] proposed a structure for a service-concept recommender system using a semantic similarity model by integrating the techniques from the view of an ontology structure-oriented metric and a concept content-oriented metric. The system was able to deliver optimal performance when compared with similar recommender systems. Li et al. [ 7 ] developed a Fuzzy linguistic modelling-based recommender system for assisting users to find experts in knowledge management systems. The developed system was applied to the aircraft industry where it demonstrated efficient and feasible performance. Lorenzi et al. [ 8 ] presented an “assumption-based multiagent” system to make travel package recommendations using user preferences in the tourism industry. It performed different tasks like discovering, filtering, and integrating specific information for building a travel package following the user requirement. Huang et al. [ 9 ] proposed a context-aware recommender system through the extraction, evaluation and incorporation of contextual information gathered using the collaborative filtering and rough set model.

In 2012, Chen et al. [ 10 ] presented a diabetes medication recommender model by using “Semantic Web Rule Language (SWRL) and Java Expert System Shell (JESS)” for aggregating suitable prescriptions for the patients. It aimed at selecting the most suitable drugs from the list of specific drugs. Mohanraj et al. [ 11 ] developed the “Ontology-driven bee’s foraging approach (ODBFA)” to accurately predict the online navigations most likely to be visited by a user. The self-adaptive system is intended to capture the various requirements of the online user by using a scoring technique and by performing a similarity comparison. Hsu et al. [ 12 ] proposed a “personalized auxiliary material” recommender system by considering the specific course topics, individual learning styles, complexity of the auxiliary materials using an artificial bee colony algorithm. Gemmell et al. [ 13 ] demonstrated a solution for the problem of resource recommendation in social annotation systems. The model was developed using a linear-weighted hybrid method which was capable of providing recommendations under different constraints. Choi et al. [ 14 ] proposed one “Hybrid Online-Product rEcommendation (HOPE) system” by the integration of collaborative filtering through sequential pattern analysis-based recommendations and implicit ratings. Garibaldi et al. [ 15 ] put forward a technique for incorporating the variability in a fuzzy inference model by using non-stationary fuzzy sets for replicating the variabilities of a human. This model was applied to a decision problem for treatment recommendations of post-operative breast cancer.

In 2013, Salehi and Kmalabadi [ 16 ] proposed an e-learning material recommender system by “modelling of materials in a multidimensional space of material’s attribute”. It employed both content and collaborative filtering. Aher and Lobo [ 17 ] introduced a course recommender system using data mining techniques such as simple K-means clustering and Association Rule Mining (ARM) algorithm. The proposed e-learning system was successfully demonstrated for “MOOC (Massively Open Online Courses)”. Kardan and Ebrahimi [ 18 ] developed a hybrid recommender system for recommending posts in asynchronous discussion groups. The system was built combining both collaborative filtering and content-based filtering. It considered implicit user data to compute the user similarity with various groups, for recommending suitable posts and contents to its users. Chang et al. [ 19 ] adopted a cloud computing technology for building a TV program recommender system. The system designed for digital TV programs was implemented using Hadoop Fair Scheduler (HFC), K-means clustering and k-nearest neighbour (KNN) algorithms. It was successful in processing huge amounts of real-time user data. Lucas et al. [ 20 ] implemented a recommender model for assisting a tourism application by using associative classification and fuzzy logic to predict the context. Niu et al. [ 21 ] introduced “Affivir: An Affect-based Internet Video Recommendation System” which was developed by calculating user preferences and by using spectral clustering. This model recommended videos with similar effects, which was processed to get optimal results with dynamic adjustments of recommendation constraints.

In 2014, Liu et al. [ 22 ] implemented a new route recommendation model for offering personalized and real-time route recommendations for self-driven tourists to minimize the queuing time and traffic jams infamous tourist places. Recommendations were carried out by considering the preferences of users. Bakshi et al. [ 23 ] proposed an unsupervised learning-based recommender model for solving the scalability problem of recommender systems. The algorithm used transitive similarities along with Particle Swarm Optimization (PSO) technique for discovering the global neighbours. Kim and Shim [ 24 ] proposed a recommender system based on “latent Dirichlet allocation using probabilistic modelling for Twitter” that could recommend the top-K tweets for a user to read, and the top-K users to follow. The model parameters were learned from an inference technique by using the differential Expectation–Maximization (EM) algorithm. Wang et al. [ 25 ] developed a hybrid-movie recommender model by aggregating a genetic algorithm (GA) with improved K-means and Principal Component Analysis (PCA) technique. It was able to offer intelligent movie recommendations with personalized suggestions. Kolomvatsos et al. [ 26 ] proposed a recommender system by considering an optimal stopping theory for delivering books or music recommendations to the users. Gottschlich et al. [ 27 ] proposed a decision support system for stock investment recommendations. It computed the output by considering the overall crowd’s recommendations. Torshizi et al. [ 28 ] have introduced a hybrid recommender system to determine the severity level of a medical condition. It could recommend suitable therapies for patients suffering from Benign Prostatic Hyperplasia.

In 2015, Zahálka et al. [ 29 ] proposed a venue recommender: “City Melange”. It was an interactive content-based model which used the convolutional deep-net features of the visual domain and the linear Support Vector Machine (SVM) model to capture the semantic information and extract latent topics. Sankar et al. [ 30 ] have proposed a stock recommender system based on the stock holding portfolio of trusted mutual funds. The system employed the collaborative filtering approach along with social network analysis for offering a decision support system to build a trust-based recommendation model. Chen et al. [ 31 ] have put forward a novel movie recommender system by applying the “artificial immune network to collaborative filtering” technique. It computed the affinity of an antigen and the affinity between an antibody and antigen. Based on this computation a similarity estimation formula was introduced which was used for the movie recommendation process. Wu et al. [ 32 ] have examined the technique of data fusion for increasing the efficiency of item recommender systems. It employed a hybrid linear combination model and used a collaborative tagging system. Yeh and Cheng [ 33 ] have proposed a recommender system for tourist attractions by constructing the “elicitation mechanism using the Delphi panel method and matrix construction mechanism using the repertory grids”, which was developed by considering the user preference and expert knowledge.

In 2016, Liao et al. [ 34 ] proposed a recommender model for online customers using a rough set association rule. The model computed the probable behavioural variations of online consumers and provided product category recommendations for e-commerce platforms. Li et al. [ 35 ] have suggested a movie recommender system based on user feedback collected from microblogs and social networks. It employed the sentiment-aware association rule mining algorithm for recommendations using the prior information of frequent program patterns, program metadata similarity and program view logs. Wu et al. [ 36 ] have developed a recommender system for social media platforms by aggregating the technique of Social Matrix Factorization (SMF) and Collaborative Topic Regression (CTR). The model was able to compute the ratings of users to items for making recommendations. For improving the recommendation quality, it gathered information from multiple sources such as item properties, social networks, feedback, etc. Adeniyi et al. [ 37 ] put forward a study of automated web-usage data mining and developed a recommender system that was tested in both real-time and online for identifying the visitor’s or client’s clickstream data.

In 2017, Rawat and Kankanhalli [ 38 ] have proposed a viewpoint recommender system called “ClickSmart” for assisting mobile users to capture high-quality photographs at famous tourist places. Yang et al. [ 39 ] proposed a gradient boosting-based job recommendation system for satisfying the cost-sensitive requirements of the users. The hybrid algorithm aimed to reduce the rate of unnecessary job recommendations. Lee et al. [ 40 ] proposed a music streaming recommender system based on smartphone activity usage. The proposed system benefitted by using feature selection approaches with machine learning techniques such as Naive Bayes (NB), Support Vector Machine (SVM), Multi-layer Perception (MLP), Instance-based k -Nearest Neighbour (IBK), and Random Forest (RF) for performing the activity detection from the mobile signals. Wei et al. [ 41 ] have proposed a new stacked denoising autoencoder (SDAE) based recommender system for cold items. The algorithm employed deep learning and collaborative filtering method to predict the unknown ratings.

In 2018, Li et al. [ 42 ] have developed a recommendation algorithm using Weighted Linear Regression Models (WLRRS). The proposed system was put to experiment using the MovieLens dataset and it presented better classification and predictive accuracy. Mezei and Nikou [ 43 ] presented a mobile health and wellness recommender system based on fuzzy optimization. It could recommend a collection of actions to be taken by the user to improve the user’s health condition. Recommendations were made considering the user’s physical activities and preferences. Ayata et al. [ 44 ] proposed a music recommendation model based on the user emotions captured through wearable physiological sensors. The emotion detection algorithm employed different machine learning algorithms like SVM, RF, KNN and decision tree (DT) algorithms to predict the emotions from the changing electrical signals gathered from the wearable sensors. Zhao et al. [ 45 ] developed a multimodal learning-based, social-aware movie recommender system. The model was able to successfully resolve the sparsity problem of recommender systems. The algorithm developed a heterogeneous network by exploiting the movie-poster image and textual description of each movie based on the social relationships and user ratings.

In 2019, Hammou et al. [ 46 ] proposed a Big Data recommendation algorithm capable of handling large scale data. The system employed random forest and matrix factorization through a data partitioning scheme. It was then used for generating recommendations based on user rating and preference for each item. The proposed system outperformed existing systems in terms of accuracy and speed. Zhao et al. [ 47 ] have put forward a hybrid initialization method for social network recommender systems. The algorithm employed denoising autoencoder (DAE) neural network-based initialization method (ANNInit) and attribute mapping. Bhaskaran and Santhi [ 48 ] have developed a hybrid, trust-based e-learning recommender system using cloud computing. The proposed algorithm was capable of learning online user activities by using the Firefly Algorithm (FA) and K-means clustering. Afolabi and Toivanen [ 59 ] have suggested an integrated recommender model based on collaborative filtering. The proposed model “Connected Health for Effective Management of Chronic Diseases”, aimed for integrating recommender systems for better decision-making in the process of disease management. He et al. [ 60 ] proposed a movie recommender system called “HI2Rec” which explored the usage of collaborative filtering and heterogeneous information for making movie recommendations. The model used the knowledge representation learning approach to embed movie-related information gathered from different sources.

In 2020, Han et al. [ 49 ] have proposed one Internet of Things (IoT)-based cancer rehabilitation recommendation system using the Beetle Antennae Search (BAS) algorithm. It presented the patients with a solution for the problem of optimal nutrition program by considering the objective function as the recurrence time. Kang et al. [ 50 ] have presented a recommender system for personalized advertisements in Online Broadcasting based on a tree model. Recommendations were generated in real-time by considering the user preferences to minimize the overhead of preference prediction and using a HashMap along with the tree characteristics. Ullah et al. [ 51 ] have implemented an image-based service recommendation model for online shopping based random forest and Convolutional Neural Networks (CNN). The model used JPEG coefficients to achieve an accurate prediction rate. Cai et al. [ 52 ] proposed a new hybrid recommender model using a many-objective evolutionary algorithm (MaOEA). The proposed algorithm was successful in optimizing the novelty, diversity, and accuracy of recommendations. Esteban et al. [ 53 ] have implemented a hybrid multi-criteria recommendation system concerned with students’ academic performance, personal interests, and course selection. The system was developed using a Genetic Algorithm (GA) and aimed at helping university students. It combined both course information and student information for increasing system performance and the reliability of the recommendations. Mondal et al. [ 54 ] have built a multilayer, graph data model-based doctor recommendation system by exploiting the trust concept between a patient-doctor relationship. The proposed system showed good results in practical applications.

In 2021, Dhelim et al. [ 55 ] have developed a personality-based product recommending model using the techniques of meta path discovery and user interest mining. This model showed better results when compared to session-based and deep learning models. Bhalse et al. [ 56 ] proposed a web-based movie recommendation system based on collaborative filtering using Singular Value Decomposition (SVD), collaborative filtering and cosine similarity (CS) for addressing the sparsity problem of recommender systems. It suggested a recommendation list by considering the content information of movies. Similarly, to solve both sparsity and cold-start problems Ke et al. [ 57 ] proposed a dynamic goods recommendation system based on reinforcement learning. The proposed system was capable of learning from the reduced entropy loss error on real-time applications. Chen et al. [ 58 ] have presented a movie recommender model combining various techniques like user interest with category-level representation, neighbour-assisted representation, user interest with latent representation and item-level representation using Feed-forward Neural Network (FNN).

Comparative chronological review

A comparative chronological review to compare the total contributions on various recommender systems in the past 10 years is given in Fig.  7 .

figure 7

Comparative chronological review of recommender systems under diverse applications

This review puts forward a comparison of the number of research works proposed in the domain of recommender systems from the year 2011 to 2021 using various deep learning and machine learning-based approaches. Research articles are categorized based on the recommender system classification framework as shown in Table 5 . The articles are ordered according to their year of publication. There are two key concepts: Application fields and techniques used. The application fields of recommender systems are divided into six different fields, viz. entertainment, health, tourism, web/e-commerce, education and social media/others.

Algorithmic categorization, simulation platforms and applications considered for various recommender systems

This section analyses different methods like deep learning, machine learning, clustering and meta-heuristic-based-approaches used in the development of recommender systems. The algorithmic categorization of different recommender systems is given in Fig.  8 .

figure 8

Algorithmic categorization of different recommender systems

Categorization is done based on content-based, collaborative filtering-based, and optimization-based approaches. In [ 8 ], a content-based filtering technique was employed for increasing the ability to trust other agents and for improving the exchange of information by trust degree. In [ 16 ], it was applied to enhance the quality of recommendations using the account attributes of the material. It achieved better performance concerning with F1-score, recall and precision. In [ 18 ], this technique was able to capture the implicit user feedback, increasing the overall accuracy of the proposed model. The content-based filtering in [ 30 ] was able to increase the accuracy and performance of a stock recommender system by using the “trust factor” for making decisions.

Different collaborative filtering approaches are utilized in recent studies, which are categorized as follows:

Model-based techniques

Neuro-Fuzzy [ 1 ] based technique helps in discovering the association between user categories and item relevance. It is also simple to understand. K-Means Clustering [ 2 , 19 , 25 , 48 ] is efficient for large scale datasets. It is simple to implement and gives a fast convergence rate. It also offers automatic recovery from failures. The decision tree [ 2 , 44 ] technique is easy to interpret. It can be used for solving the classic regression and classification problems in recommender systems. Bayesian Network [ 3 ] is a probabilistic technique used to solve classification challenges. It is based on the theory of Bayes theorem and conditional probability. Association Rule Mining (ARM) techniques [ 4 , 17 , 35 ] extract rules for projecting the occurrence of an item by considering the existence of other items in a transaction. This method uses the association rules to create a more suitable representation of data and helps in increasing the model performance and storage efficiency. Fuzzy Logic [ 5 , 7 , 15 , 20 , 28 , 43 ] techniques use a set of flexible rules. It focuses on solving complex real-time problems having an inaccurate spectrum of data. This technique provides scalability and helps in increasing the overall model performance for recommender systems. The semantic similarity [ 6 ] technique is used for describing a topological similarity to define the distance among the concepts and terms through ontologies. It measures the similarity information for increasing the efficiency of recommender systems. Rough set [ 9 , 34 ] techniques use probability distributions for solving the challenges of existing recommender models. Semantic web rule language [ 10 ] can efficiently extract the dataset features and increase the model efficiency. Linear programming-based approaches [ 13 , 42 ] are employed for achieving quality decision making in recommender models. Sequential pattern analysis [ 14 ] is applied to find suitable patterns among data items. This helps in increasing model efficiency. The probabilistic model [ 24 ] is a famous tool to handle uncertainty in risk computations and performance assessment. It offers better decision-making capabilities. K-nearest neighbours (KNN) [ 19 , 37 , 44 ] technique provides faster computation time, simplicity and ease of interpretation. They are good for classification and regression-based problems and offers more accuracy. Spectral clustering [ 21 ] is also called graph clustering or similarity-based clustering, which mainly focuses on reducing the space dimensionality in identifying the dataset items. Stochastic learning algorithm [ 26 ] solves the real-time challenges of recommender systems. Linear SVM [ 29 , 44 ] efficiently solves the high dimensional problems related to recommender systems. It is a memory-efficient method and works well with a large number of samples having relative separation among the classes. This method has been shown to perform well even when new or unfamiliar data is added. Relational Functional Gradient Boosting [ 39 ] technique efficiently works on the relational dependency of data, which is useful for statical relational learning for collaborative-based recommender systems. Ensemble learning [ 40 ] combines the forecast of two or more models and aims to achieve better performance than any of the single contributing models. It also helps in reducing overfitting problems, which are common in recommender systems.

SDAE [ 41 ] is used for learning the non-linear transformations with different filters for finding suitable data. This aids in increasing the performance of recommender models. Multimodal network learning [ 45 ] is efficient for multi-modal data, representing a combined representation of diverse modalities. Random forest [ 46 , 51 ] is a commonly used approach in comparison with other classifiers. It has been shown to increase accuracy when handling big data. This technique is a collection of decision trees to minimize variance through training on diverse data samples. ANNInit [ 47 ] is a type of artificial neural network-based technique that has the capability of self-learning and generating efficient results. It is independent of the data type and can learn data patterns automatically. HashMap [ 50 ] gives faster access to elements owing to the hashing methodology, which decreases the data processing time and increases the performance of the system. CNN [ 51 ] technique can automatically fetch the significant features of a dataset without any supervision. It is a computationally efficient method and provides accurate recommendations. This technique is also simple and fast for implementation. Multilayer graph data model [ 54 ] is efficient for real-time applications and minimizes the access time through mapping the correlation as edges among nodes and provides superior performance. Singular Value Decomposition [ 56 ] can simplify the input data and increase the efficiency of recommendations by eliminating the noise present in data. Reinforcement learning [ 57 ] is efficient for practical scenarios of recommender systems having large data sizes. It is capable of boosting the model performance by increasing the model accuracy even for large scale datasets. FNN [ 58 ] is one of the artificial neural network techniques which can learn non-linear and complex relationships between items. It has demonstrated a good performance increase when employed in different recommender systems. Knowledge representation learning [ 60 ] systems aim to simplify the model development process by increasing the acquisition efficiency, inferential efficiency, inferential adequacy and representation adequacy. User-based approaches [ 2 , 55 , 59 ] specialize in detecting user-related meta-data which is employed to increase the overall model performance. This technique is more suitable for real-time applications where it can capture user feedback and use it to increase the user experience.

Optimization-based techniques

The Foraging Bees [ 11 ] technique enables both functional and combinational optimization for random searching in recommender models. Artificial bee colony [ 12 ] is a swarm-based meta-heuristic technique that provides features like faster convergence rate, the ability to handle the objective with stochastic nature, ease for incorporating with other algorithms, usage of fewer control parameters, strong robustness, high flexibility and simplicity. Particle Swarm Optimization [ 23 ] is a computation optimization technique that offers better computational efficiency, robustness in control parameters, and is easy and simple to implement in recommender systems. Portfolio optimization algorithm [ 27 ] is a subclass of optimization algorithms that find its application in stock investment recommender systems. It works well in real-time and helps in the diversification of the portfolio for maximum profit. The artificial immune system [ 31 ]a is computationally intelligent machine learning technique. This technique can learn new patterns in the data and optimize the overall system parameters. Expectation maximization (EM) [ 32 , 36 , 38 ] is an iterative algorithm that guarantees the likelihood of finding the maximum parameters when the input variables are unknown. Delphi panel and repertory grid [ 33 ] offers efficient decision making by solving the dimensionality problem and data sparsity issues of recommender systems. The Firefly algorithm (FA) [ 48 ] provides fast results and increases recommendation efficiency. It is capable of reducing the number of iterations required to solve specific recommender problems. It also provides both local and global sets of solutions. Beetle Antennae Search (BAS) [ 49 ] offers superior search accuracy and maintains less time complexity that promotes the performance of recommendations. Many-objective evolutionary algorithm (MaOEA) [ 52 ] is applicable for real-time, multi-objective, search-related recommender systems. The introduction of a local search operator increases the convergence rate and gets suitable results. Genetic Algorithm (GA) [ 2 , 22 , 25 , 53 ] based techniques are used to solve the multi-objective optimization problems of recommender systems. They employ probabilistic transition rules and have a simpler operation that provides better recommender performance.

Features and challenges

The features and challenges of the existing recommender models are given in Table 6 .

Simulation platforms

The various simulation platforms used for developing different recommender systems with different applications are given in Fig.  9 .

figure 9

Simulation platforms used for developing different recommender systems

Here, the Java platform is used in 20% of the contributions, MATLAB is implemented in 7% of the contributions, different fold cross-validation are used in 8% of the contributions, 7% of the contributions are utilized by the python platform, 3% of the contributions employ R-programming and 1% of the contributions are developed by Tensorflow, Weka and Android environments respectively. Other simulation platforms like Facebook, web UI (User Interface), real-time environments, etc. are used in 50% of the contributions. Table 7 describes some simulation platforms commonly used for developing recommender systems.

Application focused and dataset description

This section provides an analysis of the different applications focused on a set of recent recommender systems and their dataset details.

Recent recommender systems were analysed and found that 11% of the contributions are focused on the domain of healthcare, 10% of the contributions are on movie recommender systems, 5% of the contributions come from music recommender systems, 6% of the contributions are focused on e-learning recommender systems, 8% of the contributions are used for online product recommender systems, 3% of the contributions are focused on book recommendations and 1% of the contributions are focused on Job and knowledge management recommender systems. 5% of the contributions concentrated on social network recommender systems, 10% of the contributions are focused on tourist and hotels recommender systems, 6% of the contributions are employed for stock recommender systems, and 3% of the contributions contributed for video recommender systems. The remaining 12% of contributions are miscellaneous recommender systems like Twitter, venue-based recommender systems, etc. Similarly, different datasets are gathered for recommender systems based on their application types. A detailed description is provided in Table 8 .

Performance analysis of state-of-art recommender systems

The performance evaluation metrics used for the analysis of different recommender systems is depicted in Table 9 . From the set of research works, 35% of the works use recall measure, 16% of the works employ Mean Absolute Error (MAE), 11% of the works take Root Mean Square Error (RMSE), 41% of the papers consider precision, 30% of the contributions analyse F1-measure, 31% of the works apply accuracy and 6% of the works employ coverage measure to validate the performance of the recommender systems. Moreover, some additional measures are also considered for validating the performance in a few applications.

Research gaps and challenges

In the recent decade, recommender systems have performed well in solving the problem of information overload and has become the more appropriate tool for multiple areas such as psychology, mathematics, computer science, etc. [ 80 ]. However, current recommender systems face a variety of challenges which are stated as follows, and discussed below:

Deployment challenges such as cold start, scalability, sparsity, etc. are already discussed in Sect. 3.

Challenges faced when employing different recommender algorithms for different applications.

Challenges in collecting implicit user data

Challenges in handling real-time user feedback.

Challenges faced in choosing the correct implementation techniques.

Challenges faced in measuring system performance.

Challenges in implementing recommender system for diverse applications.

Numerous recommender algorithms have been proposed on novel emerging dimensions which focus on addressing the existing limitations of recommender systems. A good recommender system must increase the recommendation quality based on user preferences. However, a specific recommender algorithm is not always guaranteed to perform equally for different applications. This encourages the possibility of employing different recommender algorithms for different applications, which brings along a lot of challenges. There is a need for more research to alleviate these challenges. Also, there is a large scope of research in recommender applications that incorporate information from different interactive online sites like Facebook, Twitter, shopping sites, etc. Some other areas for emerging research may be in the fields of knowledge-based recommender systems, methods for seamlessly processing implicit user data and handling real-time user feedback to recommend items in a dynamic environment.

Some of the other research areas like deep learning-based recommender systems, demographic filtering, group recommenders, cross-domain techniques for recommender systems, and dimensionality reduction techniques are also further required to be studied [ 83 ]. Deep learning-based recommender systems have recently gained much popularity. Future research areas in this field can integrate the well-performing deep learning models with new variants of hybrid meta-heuristic approaches.

During this review, it was observed that even though recent recommender systems have demonstrated good performance, there is no single standardized criteria or method which could be used to evaluate the performance of all recommender systems. System performance is generally measured by different evaluation matrices which makes it difficult to compare. The application of recommender systems in real-time applications is growing. User satisfaction and personalization play a very important role in the success of such recommender systems. There is a need for some new evaluation criteria which can evaluate the level of user satisfaction in real-time. New research should focus on capturing real-time user feedback and use the information to change the recommendation process accordingly. This will aid in increasing the quality of recommendations.

Conclusion and future scope

Recommender systems have attracted the attention of researchers and academicians. In this paper, we have identified and prudently reviewed research papers on recommender systems focusing on diverse applications, which were published between 2011 and 2021. This review has gathered diverse details like different application fields, techniques used, simulation tools used, diverse applications focused, performance metrics, datasets used, system features, and challenges of different recommender systems. Further, the research gaps and challenges were put forward to explore the future research perspective on recommender systems. Overall, this paper provides a comprehensive understanding of the trend of recommender systems-related research and to provides researchers with insight and future direction on recommender systems. The results of this study have several practical and significant implications:

Based on the recent-past publication rates, we feel that the research of recommender systems will significantly grow in the future.

A large number of research papers were identified in movie recommendations, whereas health, tourism and education-related recommender systems were identified in very few numbers. This is due to the availability of movie datasets in the public domain. Therefore, it is necessary to develop datasets in other fields also.

There is no standard measure to compute the performance of recommender systems. Among 60 papers, 21 used recall, 10 used MAE, 25 used precision, 18 used F1-measure, 19 used accuracy and only 7 used RMSE to calculate system performance. Very few systems were found to excel in two or more matrices.

Java and Python (with a combined contribution of 27%) are the most common programming languages used to develop recommender systems. This is due to the availability of a large number of standard java and python libraries which aid in the development process.

Recently a large number of hybrid and optimizations techniques are being proposed for recommender systems. The performance of a recommender system can be greatly improved by applying optimization techniques.

There is a large scope of research in using neural networks and deep learning-based methods for developing recommender systems. Systems developed using these methods are found to achieve high-performance accuracy.

This research will provide a guideline for future research in the domain of recommender systems. However, this research has some limitations. Firstly, due to the limited amount of manpower and time, we have only reviewed papers published in journals focusing on computer science, management and medicine. Secondly, we have reviewed only English papers. New research may extend this study to cover other journals and non-English papers. Finally, this review was conducted based on a search on only six descriptors: “Recommender systems”, “Recommendation systems”, “Movie Recommend*”, “Music Recommend*”, “Personalized Recommend*” and “Hybrid Recommend*”. Research papers that did not include these keywords were not considered. Future research can include adding some additional descriptors and keywords for searching. This will allow extending the research to cover more diverse articles on recommender systems.

Availability of data and materials

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We thank our colleagues from Assam Down Town University who provided insight and expertise that greatly assisted this research, although they may not agree with all the interpretations and conclusions of this paper.

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DR carried out the review study and analysis of the existing algorithms in the literature. MD has been involved in drafting the manuscript or revising it critically for important intellectual content. Both authors read and approved the final manuscript.

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Roy, D., Dutta, M. A systematic review and research perspective on recommender systems. J Big Data 9 , 59 (2022). https://doi.org/10.1186/s40537-022-00592-5

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Traditional recommender systems (RS) have used user-item rating histories as their primary data source, with collaborative filtering being one of the principal methods. However, generative models have recently developed abilities to model and sample from complex data distributions, including not only user-item interaction histories but also text, images, and videos - unlocking this rich data for novel recommendation tasks. Through this comprehensive and multi-disciplinary survey, we aim to connect the key advancements in RS using Generative Models (Gen-RecSys), encompassing: a foundational overview of interaction-driven generative models; the application of large language models (LLM) for generative recommendation, retrieval, and conversational recommendation; and the integration of multimodal models for processing and generating image and video content in RS. Our holistic perspective allows us to highlight necessary paradigms for evaluating the impact and harm of Gen-RecSys and identify open challenges. A more up-to-date version of the papers is maintained at: https://github.com/yasdel/LLM-RecSys.

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Health Recommender Systems: Systematic Review

Robin de croon.

1 Department of Computer Science, KU Leuven, Leuven, Belgium

Leen Van Houdt

Nyi nyi htun, gregor Štiglic.

2 Faculty of Health Sciences, University of Maribor, Maribor, Slovenia

Vero Vanden Abeele

Katrien verbert, associated data.

Coded data set of all included papers.

Overview of recommended items by 73 studies.

Overview of evaluation approaches.

Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior.

We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations.

We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review.

Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used.


There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.


Research goals.

Current health challenges are often related to our modern way of living. High blood pressure, high glucose levels, and physical inactivity are all linked to a modern lifestyle characterized by sedentary living, chronic stress, or a high intake of energy-dense foods and recreational drugs [ 1 ]. Moreover, people usually make poor decisions related to their health for distinct reasons, for example, busy lifestyles, abundant options, and a lack of knowledge [ 2 ]. Practically, all modern lifestyle health risks are directly affected by people’s health decisions [ 3 ], such as an unhealthy diet or physical inactivity, which can contribute up to three-fourth of all health care costs in the United States [ 4 ]. Most risks can be minimized, prevented, or sometimes even reversed with small lifestyle changes. Eating healthily, increasing daily activities, and knowing where to find validated health information could lead to improved health status [ 5 ].

Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior [ 6 ] and provide people with better choices and actionable knowledge based on observed behavior [ 7 - 9 ]. The overall objective of the HRS is to empower people to monitor and improve their health through technology-assisted, personalized recommendations. As one approach of modern health care is to involve patients in the cocreation of their own health, rather than just leaving it in the hands of medical experts [ 10 ], we limit the scope of this paper to HRSs that focus on laypersons, for example, nonhealth care professionals. These HRSs are different from clinical decision support systems that provide recommendations for health care professionals. However, laypersons also need to understand the rationale of recommendations, as echoed by many researchers and practitioners [ 11 ]. This paper also studies the role of a graphical user interface. To guide this study, we define our research questions (RQs) as follows:

RQ1: What are the main applications of the recent HRS, and what do these HRSs recommend?

RQ2: Which recommender techniques are being used across different HRSs?

RQ3: How are the HRSs evaluated, and are end users involved in their evaluation?

RQ4: Is a graphical user interface designed, and how is it used to communicate the recommended items to the user?

Recommender Systems and Techniques

Recommender techniques are traditionally divided into different categories [ 12 , 13 ] and are discussed in several state-of-the-art surveys [ 14 ]. Collaborative filtering is the most used and mature technique that compares the actions of multiple users to generate personalized suggestions. An example of this technique can typically be found on e-commerce sites, such as “Customers who bought this item also bought...” Content-based filtering is another technique that recommends items that are similar to other items preferred by the specific user. They rely on the characteristics of the objects themselves and are likely to be highly relevant to a user’s interests. This makes content-based filtering especially valuable for application domains with large libraries of a single type of content, such as MedlinePlus’ curated consumer health information [ 15 ]. Knowledge-based filtering is another technique that incorporates knowledge by logic inferences. This type of filtering uses explicit knowledge about an item, user preferences, and other recommendation criteria. However, knowledge acquisition can also be dynamic and relies on user feedback. For example, a camera recommender system might inquire users about their preferences, fixed or changeable lenses, and budget and then suggest a relevant camera. Hybrid recommender systems combine multiple filtering techniques to increase the accuracy of recommendation systems. For example, the companies you may want to follow feature in LinkedIn uses both content and collaborative filtering information [ 16 ]: collaborative filtering information is included to determine whether a company is similar to the ones a user already followed, whereas content information ensures whether the industry or location matches the interests of the user. Finally, recommender techniques are often augmented with additional methods to incorporate contextual information in the recommendation process [ 17 ], including recommendations via contextual prefiltering, contextual postfiltering, and contextual modeling [ 18 ].

HRSs for Laypersons

Ricci et al [ 12 ] define recommender systems as:

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [ 13 , 19 , 20 ]. The suggestions relate to various decision-making processes, such as what items to buy, what music to listen to, or what online news to read.

In this paper, we analyze how recommender systems have been used in health applications, with a focus on laypersons. Wiesner and Pfeifer [ 21 ] broadly define an HRS as:

a specialization of an RS [recommender system] as defined by Ricci et al [ 12 ]. In the context of an HRS, a recommendable item of interest is a piece of nonconfidential, scientifically proven or at least generally accepted medical information.

Researchers have sought to consolidate the vast body of literature on HRSs by publishing several surveys, literature reviews, and state-of-the-art overviews. Table 1 provides an overview of existing summative studies on HRSs that identify existing research and shows the number of studies included, the method used to analyze the studies, the scope of the paper, and their contribution.

An overview of the existing health recommender system overview papers.

a HCI: human-computer interaction.

b HRS: health recommender system.

c CTHC: computer-tailored health communication.

As can be seen in Table 1 , the scope of the existing literature varies greatly. For example, Ferretto et al [ 26 ] focused solely on HRSs in mobile apps. A total of 3 review studies focused specifically on the patient side of the HRS: (1) Calero Valdez et al [ 23 ] analyzed the existing literature from a human-computer interaction perspective and stressed the importance of a good HRS graphical user interface; (2) Schäfer et al [ 28 ] focused on tailoring recommendations to end users based on health context, history, and goals; and (3) Hors-Fraile et al [ 27 ] focused on the individual user by analyzing how HRSs can target behavior change strategies. The most extensive study was conducted by Sadasivam et al [ 29 ]. In their study, most HRSs used knowledge-based recommender techniques, which might limit individual relevance and the ability to adapt in real time. However, they also reported that the HRS has the opportunity to use a near-infinite number of variables, which enables tailoring beyond designer-written rules based on data. The most important challenges reported were the cold start [ 31 ] where limited data are available at the start of the intervention, limited sample size, adherence, and potential unintended consequences [ 29 ]. Finally, we observed that these existing summative studies were often restrictive in their final set of papers.

Our contributions to the community are four-fold. First, we analyze a broader set of research studies to gain insights into the current state of the art. We do not limit the included studies to specific devices or patients in a clinical setting but focus on laypersons in general. Second, through a comprehensive analysis, we aim to identify the applications of recent HRS apps and gain insights into actionable knowledge that HRSs can provide to users (RQ1), to identify which recommender techniques have been used successfully in the domain (RQ2), how HRSs have been evaluated (RQ3), and the role of the user interface in communicating recommendations to users (RQ4). Third, based on our extensive literature review, we derive a reference frame with five reporting guidelines for future layperson HRS research. Finally, we collected and coded a unique data set of 73 papers, which is publicly available in Multimedia Appendix 1 [ 7 - 9 , 15 , 32 - 100 ] for other researchers.

Search Strategy

This study was conducted according to the key steps required for systematic reviews according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 101 ]. A literature search was conducted using the ACM Digital Library (n=2023), IEEExplore (n=277), and PubMed (n=93) databases. As mentioned earlier, in this systematic review we focused solely on HRSs with a focus on laypersons. However, many types of systems, algorithms, and devices can be considered as a HRS. For example, push notifications in a mobile health app or health tips prompted by web services can also be considered as health-related recommendations. To outline the scope, we limited the search terms to include a recommender or recommendation, as reported by the authors. The search keywords were as follows, using an inclusive OR: ( recommender OR recommendation systems OR recommendation system ) AND (health OR healthcare OR patient OR patients ).

In addition, a backward search was performed by examining the bibliographies of the survey and review papers discussed in the Introduction section and the reference list of included studies to identify any additional studies. A forward search was performed to search for articles that cited the work summarized in Table 1 .

Study Inclusion and Exclusion Criteria

As existing work did not include many studies ( Table 1 ) and focused on a specific medical domain or device, such as mobile phones, this literature review used nonrestrictive inclusion criteria. Studies that met all the following criteria were included in the review: described an HRS whose primary focus was to improve health (eg, food recommenders solely based on user preferences [ 102 ] were not included); targeted laypersons (eg, activity recommendations targeted on a proxy user such as a coach [ 103 ] were not included); implemented the HRS (eg, papers describing an HRS concept are not included); reported an evaluation, either web-based or offline evaluation; peer-reviewed and published papers; published in English.

Papers were excluded when one of the following was true: the recommendations of HRSs were unclear; the full text was unavailable; or a newer version was already included.

Finally, when multiple papers described the same HRS, only the latest, relevant full paper was included.


To address our RQs, all included studies were coded for five distinct coding categories.

Study Details

To contextualize new insights, the publication year and publication venue were analyzed.

Recommended Items

HRSs are used across different health domains. To provide details on what is recommended, all papers were coded according to their respective health domains. To not limit the scope of potential items, no predefined coding table was used. Instead, all papers were initially coded by the first author. These resulting recommendations were then clustered together in collaboration with the coauthors into four categories, as shown in Multimedia Appendix 2 .

Recommender Techniques

This category encodes the recommender techniques that were used: collaborative filtering [ 104 ], content-based filtering [ 105 ], knowledge-based filtering [ 106 ], and their hybridizations [ 107 ]. Some studies did not specify any algorithmic details or compared multiple techniques. Finally, when an HRS used contextual information, it was coded whether they used pre- or postfiltering or contextual modeling.

Evaluation Approach

This category encodes which evaluation protocols were used to measure the effect of HRSs. We coded whether the HRSs were evaluated through offline evaluations (no users involved), surveys, heuristic feedback from expert users, controlled user studies, deployments in the wild , and randomized controlled trials (RCTs). We also coded sample size and study duration and whether ethical approval was gathered and needed.

Interface and Transparency

Recommender systems are often perceived as a black box , as the rationale for recommendations is often not explained to end users. Recent research increasingly focuses on providing transparency to the inner logic of the system [ 11 ]. We encoded whether explanations are provided and, in this case, how such transparency is supported in the user interface. Furthermore, we also classified whether the user interface was designed for a specific platform, categorized as mobile , web , or other.

Data Extraction, Intercoder Reliability, and Quality Assessment

The required information for all included technologies and studies was coded by the first author using a data extraction form. Owing to the large variety of study designs, the included studies were assessed for quality (detailed scores given in Multimedia Appendix 1 ) using the tool by Hawker et al [ 108 ]. Using this tool, the abstract and title , introduction and aims , method and data , sample size (if applicable), data analysis , ethics and bias , results , transferability or generalizability , and implications and usefulness were allocated a score between 1 and 4, with higher scoring studies indicating higher quality. A random selection with 14% (10/73) of the papers was listed in a spreadsheet and coded by a second researcher following the defined coding categories and subcategories. The decisions made by the second researcher were compared with the first. With the recommended items ( Multimedia Appendix 2 ), there was only one small disagreement between physical activity and leisure activity [ 32 ], but all other recommended items were rated exactly the same; the recommender techniques had a Cohen κ value of 0.71 ( P <.001) and the evaluation approach scored a Cohen κ value of 0.81 ( P <.001). There was moderate agreement (Cohen κ=0.568; P <.001) between the researchers concerning the quality of the papers. The interfaces used were in perfect agreement. Finally, the coding data are available in Multimedia Appendix 1 .

The literature in three databases yielded 2340 studies, of which only 23 were duplicates and 53 were full proceedings, leaving 2324 studies to be screened for eligibility. A total of 2161 studies were excluded upon title or abstract screening because they were unrelated to health or targeted at medical professionals or because the papers did not report an evaluation. Thus, the remaining 163 full-text studies were assessed for eligibility. After the removal of 90 studies that failed the inclusion criteria or met the exclusion criteria, 73 published studies remained. The search process is illustrated in Figure 1 .

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Object name is jmir_v23i6e18035_fig1.jpg

Flow diagram according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. EC: exclusion criteria; IC: inclusion criteria.

All included papers were published in 2009 or later, following an upward trend of increased popularity. The publication venues of HRSs are diverse. Only the PervasiveHealth [ 33 - 35 ], RecSys [ 36 , 37 , 109 ], and WI-IAT [ 38 - 40 ] conferences published 3 papers each that were included in this study. The Journal of Medical Internet Research was the only journal that occurred more frequently in our data set; 5 papers were published by Journal of Medical Internet Research [ 41 - 45 ]. The papers were first rated using Hawker tool [ 108 ]. Owing to a large number of offline evaluations, we did not include the sample score to enable a comparison between all included studies. The papers received an average score of 24.32 (SD 4.55, max 32; data set presented in Multimedia Appendix 1 ). Most studies scored very poor on reporting ethics and potential biases, as illustrated in Figure 2 . However, there is an upward trend over the years in more adequate reporting of ethical issues and potential biases. The authors also limited themselves to their specific case studies and did not make any recommendations for policy (last box plot is presented in Figure 2 ). All 73 studies reported the use of different data sets. Although all recommended items were health related, only Asthana et al [ 46 ] explicitly mentioned using electronic health record data. Only 14% (10/73) [ 7 , 47 - 55 ] explicitly reported that they addressed the cold-start problem.

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Object name is jmir_v23i6e18035_fig2.jpg

Distribution of the quality assessment using Hawker tool.

Most HRSs operated in different domains and thus recommended different items. In this study, four nonmutually exclusive categories of recommended items were identified: lifestyle 33% (24/73), nutrition 36% (26/73), general health information 32% (23/73), and specific health condition–related recommendations 12% (9/73). The only significant trend we found is the increasing popularity of nutrition advice . Multimedia Appendix 2 shows the distribution of these recommended items.

Many HRSs, 33% (24/73) of the included studies, suggest lifestyle-related items, but they differ greatly in their exact recommendations. Physical activity is often recommended. Physical activities are often personalized according to personal interests [ 56 ] or the context of the user [ 35 ]. In addition to physical activities, Kumar et al [ 32 ] recommend eating, shopping, and socializing activities. One study analyzes the data and measurements to be tracked for an individual and then recommends the appropriate wearable technologies to stimulate proactive health [ 46 ]. A total of 7 studies [ 7 , 9 , 42 , 53 , 57 - 59 ] more directly try to convince users to alter their behavior by recommending them to change, or alter their behavior: for example, Rabbi et al [ 7 ] learn “a user’s physical activity and dietary behavior and strategically suggests changes to those behaviors for a healthier lifestyle . ” In another example, both Marlin et al [ 59 ] and Sadasivam et al [ 42 ] motivate users to stop smoking by providing them with tailored messages, such as “Keep in mind that cravings are temporary and will pass.” Messages could reflect the theoretical determinants of quitting, such as positive outcome expectations and self-efficacy enhancing small goals [ 42 ].

The influence of food on health is also clear from the large subset of HRSs dealing with nutrition recommendations. A mere 36% (26/73) of the studies recommend nutrition-related information, such as recipes [ 50 ], meal plans [ 36 ], restaurants [ 60 ], or even help with choosing healthy items from a restaurant menu [ 61 ]. Wayman and Madhvanath [ 37 ] provide automated, personalized, and goal-driven dietary guidance to users based on grocery receipt data. Trattner and Elsweiler [ 62 ] use postfiltering to focus on healthy recipes only and extended them with nutrition advice, whereas Ge et al [ 48 ] require users to first enter their preferences for better recommendations. Moreover, Gutiérrez et al [ 63 ] propose healthier alternatives through augmented reality when the users are shopping. A total of 7 studies specifically recommend healthy recipes [ 47 , 48 , 50 , 62 , 64 - 66 ]. Most HRSs consider the health condition of the user, such as the DIETOS system [ 67 ]. Other systems recommend recipes that are synthesized based on existing recipes and recommend new recipes [ 64 ], assist parents in making appropriate food for their toddlers [ 47 ], or help users to choose allergy-safe recipes [ 65 ].

General Health Information

According to 32% (23/73) of the included studies, providing access to trustworthy health care information is another common objective. A total of 5 studies focused on personalized, trustworthy information per se [ 15 , 55 , 68 - 70 ], whereas 5 others focused on guiding users through health care forums [ 52 , 71 - 74 ]. In total, 3 studies [ 55 , 68 , 69 ] provided personalized access to general health information. For example, Sanchez Bocanegra et al [ 15 ] targeted health-related videos and augmented them with trustworthy information from the United States National Library of Medicine (MedlinePlus) [ 110 ]. A total of 3 studies [ 52 , 72 , 74 ] related to health care forums focused on finding relevant threads. Cho et al [ 72 ] built “an autonomous agent that automatically responds to an unresolved user query by posting an automated response containing links to threads discussing similar medical problems.” However, 2 studies [ 71 , 73 ] helped patients to find similar patients. Jiang and Yang [ 71 ] investigated approaches for measuring user similarity in web-based health social websites, and Lima-Medina et al [ 73 ] built a virtual environment that facilitates contact among patients with cardiovascular problems. Both studies aim to help users seek informational and emotional support in a more efficient way. A total of 4 studies [ 41 , 75 - 77 ] helped patients to find appropriate doctors for a specific health problem, and 4 other studies [ 51 , 78 - 80 ] focused on finding nearby hospitals. A total of 2 studies [ 78 , 79 ] simply focused on the clinical preferences of the patients, whereas Krishnan et al [ 111 ] “provide health care recommendations that include Blood Donor recommendations and Hospital Specialization.” Finally, Tabrizi et al [ 80 ] considered patient satisfaction as the primary feature of recommending hospitals to the user.

Specific Health Conditions

The last group of studies (9/73, 12%) focused on specific health conditions. However, the recommended items vary significantly. Torrent-Fontbona and Lopez Ibanez [ 81 ] have built a knowledge-based recommender system to assist diabetes patients in numerous cases, such as the estimated carbohydrate intake and past and future physical activity. Pustozerov et al [ 43 ] try to “reduce the carbohydrate content of the desired meal by reducing the amount of carbohydrate-rich products or by suggesting variants of products for replacement.” Li and Kong [ 82 ] provided diabetes-related information, such as the need for a low-sodium lunch, targeted on American Indians through a mobile app. Other health conditions supported by recommender systems include depression and anxiety [ 83 ], mental disorders [ 45 ], and stress [ 34 , 54 , 84 , 85 ]. Both the mental disorder [ 45 ] and the depression and anxiety [ 83 ] HRSs recommend mobile apps. For example, the app MoveMe suggests exercises tailored to the user’s mood. The HRS to alleviate stress includes recommending books to read [ 54 ] and meditative audios [ 85 ].

The recommender techniques used varied greatly. Table 2 shows the distributions of these recommender techniques.

Overview of the different recommender techniques used in the studies.

a The papers are classified based on how the authors reported their techniques.

Recommender Techniques in Practice

The majority of HRSs (49/73, 67%) rely on knowledge-based techniques, either directly (17/49, 35%) or in a hybrid approach (32/49, 65%). Knowledge-based techniques are often used to incorporate additional information of patients into the recommendation process [ 112 ] and have been shown to improve the quality of recommendations while alleviating other drawbacks such as cold-start and sparsity issues [ 14 ]. Some studies use straightforward approaches, such as if-else reasoning based on domain knowledge [ 9 , 79 , 81 , 82 , 88 , 90 , 100 ]. Other studies use more complex algorithms such as particle swarm optimization [ 57 ], fuzzy logic [ 68 ], or reinforcement algorithms [ 44 , 84 ].

In total, 32 studies reported using a combination of recommender techniques and are classified as hybrid recommender systems . Different knowledge-based techniques are often combined. For example, Ali et al [ 56 ] used a combination of rule-based reasoning, case-based reasoning, and preference-based reasoning to recommend personalized physical activities according to the user’s specific needs and personal interests. Asthana et al [ 46 ] combined the knowledge of a decision tree and demographic information to identify the health conditions. When health conditions are known, the system knows which measurements need to be monitored. A total of 7 studies used a content-based technique to recommend educational content [ 15 , 72 , 87 ], activities [ 32 , 86 ], reading materials [ 54 ], or nutritional advice [ 63 ].

Although collaborative filtering is a popular technique [ 113 ], it is not used frequently in the HRS domain. Marlin et al [ 59 ] used collaborative filtering to personalize future smoking cessation messages based on explicit feedback on past messages. This approach is used more often in combination with other techniques. A total of 2 studies [ 38 , 92 ] combined content-based techniques with collaborative filtering. Esteban et al [ 92 ], for instance, switched between content-based and collaborative approaches. The former approach is used for new physiotherapy exercises and the latter, when a new patient is registered or when previous recommendations to a patient are updated.

Context-Based Recommender Techniques

From an HRS perspective, context is described as an aggregate of various information that describes the setting in which an HRS is deployed, such as the location, the current activity, and the available time of the user. A total of 5 studies use contextual information to improve their recommendations but use a different technique; a prefilter uses contextual information to select or construct the most relevant data for generating recommendations. For example, in Narducci et al [ 75 ], the set of potentially similar patients was restricted to consultation requests in a specific medical area. Rist et al [ 33 ] applied a rule-based contextual prefiltering approach [ 114 ] to filter out inadequate recommendations, for example, “if it is dark outside, all outdoor activities, such as ‘take a walk,’ are filtered out” [ 33 ] before they are fed to the recommendation algorithm. However, a postfilter removes the recommended items after they are generated, such as filtering outdoor activities while it is raining. Casino et al [ 97 ] used a postfiltering technique by running the recommended items through a real-time constraint checker . Finally, contextual modeling, which was used by 2 studies [ 35 , 58 ], uses contextual information directly in the recommendation function as an explicit predictor of a user’s rating for an item [ 114 ].

Location, agenda, and weather are examples of contextual information used by Lin et al [ 35 ] to promote the adoption of a healthy and active lifestyle. Cerón-Rios et al [ 58 ] used a decision tree to analyze user needs, health information, interests, time, location, and lifestyle to promote healthy habits. Casino et al [ 97 ] gathered contextual information through smart city sensor data to recommend healthier routes. Similarly, contextual information was acquired by Rist et al [ 33 ] using sensors embedded in the user’s environment.


A total of 8 papers compared different recommender techniques to find the most optimal algorithm for a specific data set, end users, domain, and goal. Halder et al [ 52 ] used two well-known health forum data sets (PatientsLikeMe [ 115 ] and HealthBoards [ 116 ]) to compare 7 recommender techniques (among collaborative filtering and content-based filtering) and found that a hybrid approach scored best [ 52 ]. Another example is the study by Narducci et al [ 75 ], who compared four recommendation algorithms: cosine similarity as a baseline, collaborative filtering, their own HealthNet algorithm, and a hybrid of HealthNet and cosine similarity. They concluded that a prefiltering technique for similar patients in a specific medical area can drastically improve the recommendation accuracy [ 75 ]. The average and SD of the resulting ratings of the two collaborative techniques are compared with random recommendations by Li et al [ 60 ]. They show that a hybrid approach of a collaborative filter augmented with the calculated health level of the user performs better. In their nutrition-based meal recommender system, Yang et al [ 49 ] used item-wise and pairwise image comparisons in a two-step process. In conclusion, the 8 studies showed that recommendations can be improved when the benefits of multiple recommender techniques are combined in a hybrid solution [ 60 ] or contextual filters are applied [ 75 ].

HRSs can be evaluated in multiple ways. In this study, we found two categories of HRS evaluations: (1) offline evaluations that use computational approaches to evaluate the HRS and (2) evaluations in which an end user is involved. Some studies used both, as shown in Multimedia Appendix 3 .

Offline Evaluations

Of the total studies, 47% (34/73) do not involve users directly in their method of evaluation. The evaluation metrics also vary greatly, as many distinct metrics are reported in the included papers ( Multimedia Appendix 3 ). Precision 53% (18/34), accuracy 38% (13/34), performance 35% (12/34), and recall 32% (11/34) were the most commonly used offline evaluation metrics. Recall has been used significantly more in recent papers, whereas accuracy also follows an upward trend. Moreover, performance was defined differently across studies. Torrent-Fontbona and Lopez Ibanez [ 81 ] compared the “amount of time in the glycaemic target range by reducing the time below the target” as performance. Cho et al [ 72 ] compared the precision and recall to report the performance. Clarke et al [ 84 ] calculated their own reward function to compare different approaches, and Lin et al [ 35 ] measured system performance as the number of messages sent in their in the wild study. Finally, Marlin et al [ 59 ] tested the predictive performance using a triple cross-validation procedure.

Other popular offline evaluation metrics are accuracy-related measurements, such as mean absolute (percentage) error, 18% (6/34); normalized discounted cumulative gain (nDCG), 18% (6/34); F 1 score, 15% (5/34); and root mean square error, 15% (5/34). The other metrics were measured inconsistently. For example, Casino et al [ 97 ] reported that they measure robustness but do not outline what they measure as robustness. However, they measured the mean absolute error. Torrent-Fontbona and Lopez Ibanez [ 81 ] defined robustness as the capability of the system to handle missing values. Effectiveness is also measured with different parameters, such as its ability to take the right classification decisions [ 75 ] or in terms of key opinion leaders’ identification [ 41 ]. Finally, Li and Zaman [ 68 ] measured trust with a proxy: “evaluate the trustworthiness of a particular user in a health care social network based on factors such as role and reputation of the user in the social community” [ 68 ].

User Evaluations

Of the total papers, 53% (39/73) included participants in their HRS evaluation, with an average sample size of 59 (SD 84) participants (excluding the outlier of 8057 participants, as recruited in the study by Cheung et al [ 83 ]). On average, studies ran for more than 2 months (68, SD 56 days) and included all age ranges. There is a trend of increasing sample size and study duration over the years. However, only 17 studies reported the study duration; therefore, these trends were not significant. Surveys (12/39, 31%), user studies (10/39, 26%), and deployments in the wild (10/39, 26%) were the most used user evaluations. Only 6 studies used an RCT to evaluate their HRS. Finally, although all the included studies focused on HRSs and were dealing with sensitive data, only 12% (9/73) [ 9 , 34 , 42 - 45 , 73 , 83 , 95 ] reported ethical approval by a review board.

No universal survey was found, as all the studies deployed a distinct survey. Ge et al [ 48 ] used the system usability scale and the framework of Knijnenburg et al [ 117 ] to explain the user experience of recommender systems. Esteban et al [ 95 ] designed their own survey with 10 questions to inquire about user experience. Cerón-Rios [ 58 ] relied on the ISO/IEC (International Organization of Standardization/International Electrotechnical Commission) 25000 standard to select 7 usability metrics to evaluate usability. Although most studies did not explicitly report the surveys used, user experience was a popular evaluation metric, as in the study by Wang et al [ 69 ]. Other metrics range from measuring user satisfaction [ 69 , 99 ] and perceived prediction accuracy [ 59 ] (with 4 self-composed questions). Nurbakova et al [ 98 ] combined data analytics with surveys to map their participants’ psychological background, including orientations to happiness measured using the Peterson scale [ 118 ], personality traits using the Mini-International Personality Item Pool [ 119 ], and Fear of Missing Out based on the Przybylski scale [ 120 ].

Single-Session Evaluations (User Studies)

A total of 10 studies recruited users and asked them to perform certain tasks in a single session. Yang et al [ 49 ] performed a 60-person user study to assess its feasibility and effectiveness. Each participant was asked to rate meal recommendations relative to those made using a traditional survey-based approach. In a study by Gutiérrez et al [ 63 ], 15 users were asked to use the health augmented reality assistant and measure the qualities of the recommender system, users’ behavioral intentions, perceived usefulness, and perceived ease of use. Jiang and Xu [ 77 ] performed 30 consultations and invited 10 evaluators majoring in medicine and information systems to obtain an average rating score and nDCG. Radha et al [ 8 ] used comparative questions to evaluate the feasibility. Moreover, Cheng et al [ 89 ] used 2 user studies to rank two degrees of compromise (DOC). A low DOC assigns more weight to the algorithm, and a high DOC assigns more weight to the user’s health perspective. Recommendations with a lower DOC are more efficient for the user’s health, but recommendations with a high DOC could convince users to believe that the recommended action is worth doing. Other approaches used are structured interviews [ 58 ], ranking [ 86 , 89 ], asking for unstructured feedback [ 40 , 88 ], and focus group discussions [ 87 ]. Finally, 3 studies [ 15 , 75 , 90 ] evaluated their system through a heuristic evaluation with expert users.

In the Wild

Only 2 studies tested their HRS into the wild recruited patients (people with a diagnosed health condition) in their evaluation. Yom-Tov et al [ 44 ] provided 27 sedentary patients with type 2 diabetes with a smartphone-based pedometer and a personal plan for physical activity. They assessed the effectiveness by calculating the amount of activity that the patient performed after the last message was sent. Lima-Medina et al [ 73 ] interviewed 45 patients with cardiovascular problems after a 6-month study period to measure (1) social management results, (2) health care plan results, and (3) recommendation results. Rist et al [ 33 ] performed an in-situ evaluation in an apartment of an older couple and used the data logs to describe the usage but augmented the data with a structured interview.

Yang et al [ 49 ] conducted a field study of 227 anonymous users that consisted of a training phase and a testing phase to assess the prediction accuracy. Buhl et al [ 99 ] created three user groups according to the recommender technique used and analyzed log data to compare the response rate, open email rate, and consecutive log-in rate. Similarly, Huang et al [ 76 ] compared the ratio of recommended doctors chosen and reserved by patients with the recommended doctors. Lin et al [ 35 ] asked 6 participants to use their HRSs for 5 weeks, measured system performance, studied user feedback to the recommendations, and concluded with an open-user interview. Finally, Ali et al [ 56 ] asked 10 volunteers to use their weight management systems for a couple of weeks. However, they do not focus on user-centric evaluation, as “only a prototype of the [...] platform is implemented.”

Rabbi et al [ 7 ] followed a single case with multiple baseline designs [ 121 ]. Single-case experiments achieve sufficient statistical power with a large number of repeated samples from a single individual. Moreover, Rabbi et al [ 7 ] argued that HRSs suit this requirement “since enough repeated samples can be collected with automated sensing or daily manual logging [ 121 ].” Participants were exposed to 2, 3, or 4 weeks of the control condition. The study ran for 7-9 weeks to compensate for the novelty effects. Food and exercise log data were used to measure changes in food calorie intake and calorie loss during exercise.

Randomized Controlled Trials

Only 6 studies followed an RCT approach. In the RCT by Bidargaddi et al [ 45 ], a large group of patients (n=192) and control group (n=195) were asked to use a web-based recommendation service for 4 weeks that recommended mental health and well-being mobile apps. Changes in well-being were measured using the Mental Health Continuum-Short Form [ 122 ]. The RCT by Sadasivam et al [ 42 ] enrolled 120 current smokers (n=74) and control group (n=46) as a follow-up to a previous RCT [ 123 ] that evaluated their portal to specifically evaluate the HRS algorithm. Message ratings were compared between the intervention and control groups.

Cheung et al [ 83 ] measured app loyalty through the number of weekly app sessions over a period of 16 weeks with 8057 users. In the study by Paredes et al [ 34 ], 120 participants had to use the HRS for at least 26 days. Self-reported stress assessment was performed before and after the intervention. Agapito et al [ 67 ] used an RCT with 40 participants to validate the sensitivity (true positive rate/[true positive rate+false negative rate]) and specificity (true negative rate/[true negative rate+false positive rate]) of the DIETOS HRS. Finally, Luo et al [ 93 ] performed a small clinical trial for more than 3 months (but did not report the number of participants). Their primary outcome measures included two standard clinical blood tests: fasting blood glucose and laboratory-measured glycated hemoglobin, before and after the intervention.

Only 47% (34/73) of the studies reported implementing a graphical user interface to communicate the recommended health items to the user. As illustrated in Table 3 , 53% (18/34) use a mobile interface, usually through a mobile (web) app, whereas 36% (14/34) use a web interface to show the recommended items. Rist et al [ 33 ] built a kiosk into older adults’ homes, as illustrated in Figure 3 . Gutiérrez et al [ 63 ] used Microsoft HoloLens to project healthy food alternatives in augmented reality surrounding a physical object that the user holds, as shown in Figure 4 .

Distribution of the interfaces used among the different health recommender systems (n=34).

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Rist et al installed a kiosk in the home of older adults as a direct interface to their health recommender system.

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An example of the recommended healthy alternatives by Gutiérrez et al.


A total of 7 studies [ 33 , 34 , 37 , 63 , 79 , 88 , 97 ] or approximately one-fourth of the studies with an interface included visualizations. However, the approach used was different for all studies, as shown in Table 4 . Showing stars to show the relevance of a recommended item are only used by Casino et al [ 97 ] and Gutiérrez et al [ 63 ]. Wayman and Madhvanath [ 37 ] also used bar charts to visualize the progress toward a health goal. They visualize the healthy proportions, that is, what the user should eat. Somewhat more complex visualizations are used by Ho and Chen [ 88 ] who visualized the user’s ECG zones. Paredes et al [ 34 ] presented an emotion graph as an input screen. Rist et al [ 33 ] visualized an example of how to perform the recommended activity.

Distribution of the visualizations used among the different health recommender systems (n=7).


In the study by Lage et al [ 87 ], participants expressed that:

they would like to have more control over recommendations received. In that sense, they suggested more information regarding the reasons why the recommendations are generated and more options to assess them.

A total of 7 studies [ 7 , 37 , 41 , 45 , 63 , 66 , 82 ] explained the reasoning behind recommendations to end users at the user interface. Gutiérrez et al [ 63 ] provided recommendations for healthier food products and mentioned that the items ( Figure 4 ) are based on the users’ profile. Ueta et al [ 66 ] explained the relationship between the recommended dishes and a person’s health conditions. For example, a person with acne can see the following text: “15 dishes that contained Pantothenic acid thought to be effective in acne a lot became a hit” [ 66 ]. Li and Kong [ 82 ] showed personalized recommended health actions in a message center. Color codes are used to differentiate between reminders, missed warnings, and recommendations. Rabbi et al [ 7 ] showed tailored motivational messages to explain why activities are recommended. For example, when the activity walk near East Ave is recommended, the app shows the additional message:

1082 walks in 240 days, 20 mins of walk everyday. Each walk nearly 4 min. Let us get 20 mins or more walk here today 7

Wayman and Madhvanath [ 37 ] first visualized the user’s personal nutrition profile and used the lower part of the interface to explain why the item was recommended. They provided an illustrative example of spaghetti squash. The explanation shows that:

This product is high in Dietary_fiber, which you could consume more of. Try to get 3 servings a week 37

Guo et al [ 41 ] recommended doctors and showed a horizontal bar chart to visualize the user’s values compared with the average values. Finally, Bidargaddi et al [ 45 ] visualized how the recommended app overlaps with the goal set by the users, as illustrated in Figure 5 .

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A screenshot from the health recommender system of Bidargaddi et al. Note the blue tags illustrating how each recommended app matches the users’ goals.

Principal Findings

HRSs cover a multitude of subdomains, recommended items, implementation techniques, evaluation designs, and means of communicating the recommended items to the target user. In this systematic review, we clustered the recommended items into four groups: lifestyle, nutrition, general health care information, and specific health conditions. There is a clear trend toward HRSs that provide well-being recommendations but do not directly intervene in the user’s medical status. For example, almost 70% (50/73; lifestyle and nutrition) focused on no strict medical recommendations. In the lifestyle group, physical activities (10/24, 42%) and advice on how to potentially change behavior (7/24, 29%) were recommended most often. In the nutrition group, these recommendations focused on nutritional advice (8/26, 31%), diets (7/26, 27%), and recipes (7/26, 27%). A similar trend was observed in the health care information group, where HRSs focused on guiding users to the appropriate environments such as hospitals (5/23, 22%) and medical professionals (4/23, 17%) or on helping users find qualitative information (5/23, 22%) on validated sources or from experiences by similar users and patients on health care forums (3/23, 13%). Thus, they only provide general information and do not intervene by recommending, for example, changing medication. Finally, when HRSs targeted specific health conditions, they recommended nonintervening actions, such as meditation sessions [ 84 ] or books to read [ 54 ].

Although collaborative filtering is commonly the most used technique in other domains [ 124 ], here only 3 included studies reported the use of a collaborative filtering approach. Moreover, 43% (32/73) of the studies applied a hybrid approach, showing that HRS data sets might need special attention, which might also be the reason why all 73 studies used distinct data sets. In addition, the HRS evaluations varied greatly and were divided over evaluations where the end user was involved and evaluations that did not evolve users (offline evaluations). Only 47% (34/73) of the studies reported implementing a user interface to communicate recommendations to the user, despite the need to show the rationale of recommendations, as echoed by many researchers and practitioners [ 11 ]. Moreover, only 15% (7/47) included a (basic) visualization.

Unfortunately, this general lack of agreement on how to report HRSs might introduce researcher bias, as a researcher is currently completely unconstrained in defining what and how to measure the added value of an HRS. Therefore, further debate in the health recommender community is needed on how to define and measure the impact of HRSs. On the basis of our review and contribution to this discussion, we put forward a set of essential information that researchers should report in their studies.

Considerations for Practice

The previously discussed results have direct implications in practice and provide suggestions for future research. Figure 6 shows a reference frame of these requirements that can be used in future studies as a quality assessment tool.

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A reference frame to report health recommender system studies. On the basis of the results of this study, we suggest that it should be clear what and how items are recommended (A), who the target user is (B), which data are used (C), and which recommender techniques are applied (D). Finally, the evaluation design should be reported in detail (E).

Define the Target User

As shown in this review, HRSs are used in a plethora of subdomains and each domain has its own experts. For example, in nutrition, the expert is most likely a dietician. However, the user of an HRS is usually a layperson without the knowledge of these domain experts, who often have different viewing preferences [ 125 ]. Furthermore, each user is unique. All individuals have idiosyncratic reasons for why they act, think, behave, and feel in a certain way at a specific stage of their life [ 126 ]. Not everybody is motivated by the same elements. Therefore, it is important to know the target user of the HRS. What is their previous knowledge, what are their goals, and what motivates them to act on a recommended item?

Show What Is Recommended (and How)

Researchers have become aware that accuracy is not sufficient to increase the effectiveness of a recommender system [ 127 ]. In recent years, research on human factors has gained attention. For example, He et al [ 11 ] surveyed 24 existing interactive recommender systems and compared their transparency, justification, controllability, and diversity. However, none of these 24 papers discussed HRSs. This indicates the gap between HRSs and recommender systems in other fields. Human factors have gained interest in the recommender community by “combining interactive visualization techniques with recommendation techniques to support transparency and controllability of the recommendation process” [ 11 ]. However, in this study, only 10% (7/73) explained the rationale of recommendations and only 10% (7/73) included a visualization to communicate the recommendations to the user. We do not argue that all HRSs should include a visualization or an explanation. However, researchers should pay attention to the delivery of these recommendations. Users need to understand, believe, and trust the recommended items before they can act on it.

To compare and assess HRSs, researchers should unambiguously report what the HRS is recommending. After all, typical recommender systems act like a black box , that is, they show suggestions without explaining the provenance of these recommendations [ 11 ]. Although this approach is suitable for typical e-commerce applications that involve little risk, transparency is a core requirement in higher risk application domains such as health [ 128 ]. Users need to understand why a recommendation is made, to assess its value and importance [ 12 ]. Moreover, health information can be cumbersome and not always easy to understand or situate within a specific health condition [ 129 ]. Users need to know whether the recommended item or action is based on a trusted source, tailored to their needs, and actionable [ 130 ].

Report the Data Set Used

All 73 studies used a distinct data set. Furthermore, some studies combine data from multiple databases, making it even more difficult to judge the quality of the data [ 35 ]. Nonetheless, most studies use self-generated data sets. This makes it difficult to compare and externally validate HRSs. Therefore, we argued that researchers should clarify the data used and potentially share whether these data are publicly available. However, in health data are often highly privacy sensitive and cannot be shared among researchers.

Outline the Recommender Techniques

The results show that there is no panacea for which recommender technique to use. The included studies differ from logic filters to traditional recommender techniques, such as collaborative filtering and content-based filtering to hybrid solutions and self-developed algorithms. However, with 44% (32/73), there is a strong trend toward the use of hybrid recommender techniques. The low number of collaborative filter techniques might be related to the fact that the evaluation sample sizes were also relatively low. Unfortunately, some studies have not fully disclosed the techniques used and only reported on the main algorithm used. It is remarkable that studies published in high-impact journals, such as studies by Bidargaddi et al [ 45 ] and Cheung et al [ 83 ], did not provide information on the recommender technique used. Nonetheless, disclosing the recommender technique allows other researchers not only to build on empirically tested technologies but also to verify whether key variables are included [ 29 ]. User data and behavior data can be identified to augment theory-based studies [ 29 ]. Researchers should prove that the algorithm is capable of recommending valid and trustworthy recommendations to the user based on their available data set.

Elaborate on the Evaluation Protocols

HRSs can be evaluated using different evaluation protocols. However, the protocol should be outlined mainly by the research goals of the authors. On the basis of the papers included in this study, we differentiate between the two approaches. In the first approach, the authors aim to influence their users’ health, for example, by providing personalized diabetes guidelines [ 81 ] or prevention exercises for users with low back pain [ 95 ]. Therefore, the end user should always be involved in both the design and evaluation processes. However, only 8% (6/73) performed an RCT and 14% (10/73) deployed their HRS in the wild. This lack of user involvement has been noted previously by researchers and has been identified as a major challenge in the field [ 27 , 28 ]. Nonetheless, in other domains, such as job recommenders [ 131 ] or agriculture [ 132 ], user-centered design has been proposed as an important methodology in the design and development of tools used by end users, with the purpose of gaining trust and promoting technology acceptance, thereby increasing adoption with end users. Therefore, we recommend that researchers evaluate their HRSs with actual users. A potential model for a user-centric approach to recommender system evaluation is the user-centric framework proposed by Knijnenburg et al [ 117 ].

Research protocols need to be elaborated and approved by an ethical review board to prevent any impact on users. Authors should report how they informed their users and how they safeguarded the privacy of the users. This is in line with the modern journal and conference guidelines. For example, editorial policies of the Journal of Medical Internet Research state that “when reporting experiments on human subjects, authors should indicate IRB (Institutional Rese[a]rch Board, also known as REB) approval/exemption and whether the procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation” [ 133 ]. However, only 12% (9/73) reported their approval by an ethical review board. Acquiring review board approval will help the field mature and transition from small incremental studies to larger studies with representative users to make more reliable and valid findings.

In the second approach, the authors aim to design a better algorithm, where better is again defined by the authors. For example, the algorithm might perform faster, be more accurate, and be more efficient in computing power. Although the F 1 score, the mean absolute error, and nDCG are well defined and known within the recommender domain, other parameters are more ambiguous. For example, the performance or effectiveness can be assessed using different measurements. However, a health parameter can be monitored, such as the duration that a user remains within healthy ranges [ 81 ]. Furthermore, it could be a predictive parameter, such as an improved precision and recall as a proxy for performance [ 72 ]. Unfortunately, this difference makes it difficult to compare health recommendation algorithms. Furthermore, this inconsistency in measurement variables makes it infeasible to report in this systematic review which recommender techniques to use. Therefore, we argue that HRS algorithms should always be evaluated for other researchers to validate the results, if needed.


This study has some limitations that affect its contribution. Although an extensive scope search was conducted in scientific databases and most relevant health care informatic journals, some relevant literature in other domains might have been excluded. The keywords used in the search string could have impacted the results. First, we did not include domain-specific constructs of health, such as asthma, pregnancy, and iron deficiency. Many studies may implicitly report healthy computer-generated recommendations when they research the impact of a new intervention. In these studies, however, building an HRS is often not their goal and, therefore, was excluded from this study. Second, we searched for papers that reported studying an HRS; nonincluded studies might have built an HRS but did not report it as such. Considering our RQs, we deemed it important that authors explicitly reported their work as a recommender system. To conclude, in this study, we provide a large cross-domain overview of health recommender techniques targeted to laypersons and deliver a set of recommendations that could help the field of HRS mature.

This study presents a comprehensive report on the use of HRS across domains. We have discussed the different subdomains HRS applied in, the different recommender techniques used, the different manners in which they are evaluated, and finally, how they present the recommendations to the user. On the basis of this analysis, we have provided research guidelines toward a consistent reporting of HRSs. We found that although most applications are intended to improve users’ well-being, there is a significant opportunity for HRSs to inform and guide users’ health actions. Although many of the studies present a lack of a user-centered evaluation approach, some studies performed full-scale RCT evaluations or elaborated in the wild studies to validate their HRS, showing the field of HRS is slowly maturing. On the basis of this study, we argue that it should always be clear what the HRS is recommending and to whom these recommendations are for. Graphical assets should be added to show how recommendations are presented to users. Authors should also report which data sets and algorithms were used to calculate the recommendations. Finally, detailed evaluation protocols should be reported.

We conclude that the results motivate the creation of richer applications in future design and development of HRSs. The field is maturing, and interesting opportunities are being created to inform and guide health actions.


This work was part of the research project PANACEA Gaming Platform with project HBC.2016.0177, which was financed by Flanders Innovation & Entrepreneurship and research project IMPERIUM with research grant G0A3319N from the Research Foundation-Flanders (FWO) and the Slovenian Research Agency grant ARRS-N2-0101. Project partners were BeWell Innovations and the University Hospital of Antwerp.


Multimedia appendix 1, multimedia appendix 2, multimedia appendix 3.

Conflicts of Interest: None declared.

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Research Article

A collaborative approach for research paper recommender system

Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Writing – original draft, Writing – review & editing

Affiliations Department of Computer Science, Faculty of Computer Science and Information Technology, Bayero University, Kano, Nigeria, Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

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Roles Supervision, Validation, Visualization, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

Roles Funding acquisition, Resources

Affiliation Sekolah Tinggi Pariwisata Ambarrukmo, Yogyakarta, Indonesia

Affiliation Faculty of Information Technology and Business, Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Roles Formal analysis, Investigation, Project administration, Resources, Validation, Visualization, Writing – review & editing

Affiliations Faculty of Information Technology and Business, Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia, AMCS Research Center, Yogyakarta, Indonesia

  • Khalid Haruna, 
  • Maizatul Akmar Ismail, 
  • Damiasih Damiasih, 
  • Joko Sutopo, 
  • Tutut Herawan


  • Published: October 5, 2017
  • https://doi.org/10.1371/journal.pone.0184516
  • Reader Comments

Fig 1

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

Citation: Haruna K, Akmar Ismail M, Damiasih D, Sutopo J, Herawan T (2017) A collaborative approach for research paper recommender system. PLoS ONE 12(10): e0184516. https://doi.org/10.1371/journal.pone.0184516

Editor: Feng Xia, Dalian University of Technology, CHINA

Received: June 10, 2017; Accepted: August 27, 2017; Published: October 5, 2017

Copyright: © 2017 Haruna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All study files are available from: https://figshare.com/articles/Supporting_Information_Dataset_docx/5368408 .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The overabundance of information that is available over the internet makes information seeking a difficult task. Researchers find it difficult to access and keep track of the most relevant and promising research papers of their interest [ 1 ]. The easiest and the most common approach used in searching for related publications is to send a query message asking the web to provide you with specific information [ 2 ]. However, the results from this approach largely depend on how good the user is in fine-tuning the query message beside its inability to personalize the searching results.

Another classical approach used by most researchers is to follow the list of references from the documents they already possessed [ 3 ]. Even though this approach might be quite effective in some instances, it does not guarantee full coverage of recommending research papers and cannot trace papers published after the possessed paper. In addition, the list of references may not be publicly available and therefore hard for the researchers to access.

An alternative approach that has been proposed in the literature is the use of research paper recommender systems [ 4 , 5 ], to automatically suggest relevant papers to the researchers based on some initial information provided by the users that are more elaborate than a few keywords.

To provide more accurate and relevant recommendations, recommender systems incorporate the users’ contexts and the possible contextual information of the consumed contents [ 6 ]. Different researchers proposed the use of a different user provided information such as the use of a list of citations [ 7 ], the list of papers authored by an author [ 8 ], part of paper text [ 2 ], a single paper [ 9 ], and so on. In these approaches, a user profile is constructed from this initial information to represent the interests of the users and the system search for items or other profiles similar to the one provided to generate recommendations. The challenge was not just to provide a very rich recommendation to researchers at any time, any place and in any form but to also offer the right paper to the right researcher in the right way [ 10 – 12 ].

The major limitation of the existing approaches is their assumption of the availability of the whole content of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. In an attempt to address this problem, Liu, et al . in [ 3 ] applied the concept of the collaborative approach to mine the hidden associations that exists between a target paper and its references to provide a unique and useful list of research papers as recommendations.

Motivated from [ 3 ], this paper presents a collaborative approach for research paper recommender system. In addition to mining the hidden associations between a target paper and its references, in this paper, we also put into cognizance the hidden associations between the target paper’s citations (see section 3). Similar to [ 3 ], our task is not to apply a direct relation between paper-citation relations because, in one way or the other, a researcher who is in possession of a research paper directly or indirectly has access to its limited references and also to its citations. Our aim is to identify the latent associations that exist between research papers based on the perspective of paper-citation relations. A candidate paper is qualified for consideration in [ 3 ] if it cited any of the target paper’s references. In our proposed approach, a candidate paper is qualified for consideration if and only if it cited any of the target paper’s references and there exist another paper which cited both the candidate and the target papers simultaneously. We then measure and weigh the extent of similarity between the target paper and the qualified candidate papers and recommend the top-N most similar papers based on the assumption that if there exist significant co-occurrence between the target paper and the qualified candidate papers, then there exist some extent of similarities between them. This strictness in qualifying a candidate paper helps in enhancing the overall performance of the approach and the ability to return relevant and useful recommendations at the top of the recommendation list.

The major contributions of our proposed approach are as follows;

  • We utilized the advantages of publicly available contextual metadata to propose an independent research paper that does not require a priori user profile.
  • Our approach provides personalized recommendations regardless of the research field and regardless of user expertise.

The outline of the rest of the paper is as follows. We first present some related works on recommending research papers. We then detailed our proposed approach. Next, we described our experiments, starting with the dataset and the baseline methods, followed by the evaluation procedures. We then discuss our findings and lastly conclude the paper with a brief concluding remark and future research directions.

2. Related work

Research paper recommenders that provide the best suggestions for all alternatives emerged over the last decade to help researchers on seemingly finding works of their interest over the Cyber Ocean of information. Collaborative filtering (CF) is one of the most successful techniques used in recommender systems [ 13 ]. It is a method which recommends items to target users based on what other similar users have previously preferred [ 14 – 16 ]. It has been used in various applications such as in recommending movies [ 17 ], audio CD [ 18 ], e-commerce [ 19 ], music [ 20 ], Usenet news [ 16 ], research papers [ 7 , 21 – 24 ] among others (see [ 25 ]). Some researchers [ 13 , 21 , 26 ], have criticized the use of this technique to recommend scholarly papers. Precisely the authors in [ 21 , 26 ], claimed that collaborative filtering is only effective in a domain where the number of users seeking recommendation is higher than the number of items to be recommended, such domains include movies [ 27 ], music [ 28 ], news [ 29 ] etc. While the argument in [ 13 ], is that researchers are not willing to spend their valuable time to provide explicit ratings to their consumed research papers, and therefore, leading to insufficient ratings by the researchers to the research papers. Furthermore, for a user to receive useful recommendations, a tangible number of ratings is required.

Nevertheless, despite these aforementioned problems, a significant amount of papers can be traced, which suggest relevant papers to researchers based on collaborative filtering by mining latent associations between scholarly papers. These associations are either directly obtained by taking into consideration paper citations as rating scores [ 7 ], or by monitoring the researchers’ actions implicitly [ 30 , 31 ]. Applying citation analysis such as bibliographical coupling [ 32 ] and co-citation analysis [ 33 ] has also been used to identify similar papers to a target paper [ 34 ].

The relationships among research papers have been categorized into direct and indirect relations in a survey conducted by [ 35 ]. In the paper, three approaches were identified for detecting the relationships between papers based on the perspective of paper sources. Namely, citation context, citation analysis, and content-based. The authors claimed that content-based approach becomes less appropriate in detecting relationships across research papers, due to its inability to accommodate some specific characteristics that exist in the research papers like author and citations. Therefore, it becomes suitable only for identifying similarity relations across regular documents. On the other hand, the use of citation analysis can generate more relations between research papers but cannot generate relations from semantic text. This weakness is addressed by using citation context based approach, which depicts more emphasis on determining some important features in the text classification process to increase classification performance.

A context-based collaborative framework (CCF) that uses only easily obtained citations relations as source data was proposed in [ 3 ]. The framework employs an association-mining technique to obtain a paper representation of the paper citation context. A pairwise comparison was then performed to compute the extent of similarities between papers. The use of collaborative filtering has also been explored in [ 7 ], by using citation-web between scholarly papers to create a rating matrix. The aim was to use the paper-citation relation to recommend some additional references to the input paper. In doing that, the authors investigated the use of six different algorithms for selecting citations. Using offline evaluation, they discovered large disparity in the returned accuracy by each of the six algorithms.

The authors in [ 6 ], hypothesized the author’s previous publications to constitute a clear signal of the latent interests of a researcher. The key part of their model was to enhance the user profile with the information coming directly from the references to the researcher’s previous works as well as the papers that cited them. However, the approach increases the well-known sparsity problem. To alleviate this problem, they extend their work in [ 8 ], to mine potential citations papers using imputed similarities through the use of collaborative filtering. They also refined the use of citing papers in characterizing a target candidate paper using fragments in the citation and potential citation papers. Whilst the approach works well for researchers with a single discipline, it generates poor results for the multidisciplinary researchers. To overcome this problem, an adaptive neighbor selection approach was proposed in [ 2 ], to overcome imputation-based collaborative filtering problem. Whereas authors in [ 2 , 6 , 8 ], recommend papers relevant to the researcher’s interest, they also addressed the serendipitous scholarly paper recommendation in [ 36 ].

On another development, the increasing number of research communities and social networking sites such as LiveJournal and MySpace have brought new opportunities for research paper recommendation systems. Researches show that users in online social networks tend to form knit groups [ 37 ], with strongly large connected components [ 38 ].

Several kinds of research have considered the social group formation and community membership in social networks and their use in recommender systems [ 39 – 46 ]. These researchers utilized the influence of social properties to suggest relevant information to individual or group of users based on social ties, which can either be strong or weak depending on the tie strength that represents the closeness and interaction frequency between the information source and recipient [ 47 , 48 ].

Recommendations from strong ties are believed to be more persuasive than those from weak ties [ 49 – 51 ]. This is because information transferred by strong ties is likely to be perceived as more relevant and reliable. To be specific, the authors of [ 45 , 52 ] proposed a novel algorithm called socially aware recommendation of scholarly papers (SARSP) that utilizes the aspect of social learning and networking for conference participants through the construction of relations in folksonomies and social ties. The algorithm recommends research papers issued by an active participant to other conference participants based on the computation of their social ties. This approach has been extended in [ 53 ], to include personality behavior in addition to social relations among smart conference attendees. A more detail survey on scholarly data is presented in [ 54 ] for more exploration.

The major challenge with the previous researches is that all the contextual information from the recommended, referenced and cited papers must be fully accessible to the recommenders, which are not always freely available due to factors such as copyright restrictions. Another major problem with the existing research paper recommender systems is their dependency on a priori user profile, which makes the system to work well only when it already has a number of registered users, a major hurdle for the construction of new recommender system. Furthermore, the recommendation coverage of most of the current paper recommenders are limited to a certain field of research, this is because recommending papers are stored prior and therefore the system cannot effectively scan the entire databases to find connections between papers. Moreover, most of the existing research paper frameworks are designed to work only on a single discipline, and therefore cannot be used to address the problems of multidisciplinary scholars. While the use of keyword-based query information retrieval technique through search engines is able to scan all document for relevant text, it also provides 100s of irrelevant documents, besides its inability to provide personalize results to the individual researchers.

Different from the existing works, in this paper, we propose a new approach based on collaborative filtering that utilizes only publicly available contextual metadata to personalize recommendations based on the hidden associations that exist between research papers. Our proposed approach does not only provide personalized recommendations regardless of the research field and regardless of user expertise but also handles multi-disciplinary problems.

3. Proposed collaborative research paper recommendation approach

Even though some researchers [ 6 , 13 , 21 , 26 ], claimed content based to be the most suitable approach when dealing with scholarly domain, other researchers [ 35 ] argued on its suitability because only become suitable in identifying similarity relations across regular documents but lacks some important features to effectively detect relationships across research papers.

In this paper, we are motivated to leverage the advantages of collaborative approach as it has proved to be effective in the domains of movies [ 27 ], music [ 28 ], news [ 29 ], e-commerce [ 19 ], etc. The unsuitability of the collaborative approach to research paper recommenders was referred to the lack of ratings to research papers by the researchers [ 13 ]. In bringing a solution to this problem, we mined rating score between researchers and research papers based on paper-citation relations. We use C ij to denote citation score between paper i and a cited-paper j from a paper-citation matrix C . If paper i cited a paper j , C ij = 1 otherwise C ij = 0.

We initiate our approach by first transforming all the recommending papers (in our dataset) into a paper-citation relations matrix in which, the rows and the columns respectively represent the recommending papers and their citations. Our approach aimed to deal with scenarios in which: (a) A researcher who finds an interesting paper after some initial searches, wants to get more other related papers similar to it. (b) A student received a paper by his supervisor to start a research in the topic area covered by it. (c) A reviewer wants to explore more based on a received paper that addresses a subject matter which he is not a specialist in. (d) A researcher who wants to explore more from his previous publication(s). In all these cases, we consider a situation where the references and citations of the possessed paper that indicate the user’s preferences are publicly available (which is usually the case in almost all the major academic databases).

Algorithm 1. Algorithm representing proposed approach.

Algorithm: Collaborative Research Paper Recommendation

Input: Target Paper

Output: Top-N Recommendation

Given a target paper p i as a query,

  • For each of the references Rf j , extract all other papers p ci that also cited Rf j other than the target paper p i .
  • For each of the citations Cf j , extract all other papers p ri that Cf j referenced other than the target paper p i .
  • Qualify all the candidate papers p c from p ci that has been referenced by at least any of the p ri

research papers on recommender systems

  • Recommend the top-N most similar papers to the user.

We accept the user’s query in order to identify the target-paper. Once the target paper is identified, we apply algorithm 1. The algorithm retrieves all the target paper’s references and citations. For each of the references, it extracts all other papers from the web (google scholar to be precise) that also cited any of those target paper’s references. In addition, for each of the target paper’s citations, it extracts all other papers from the web that referenced any of those target paper’s citations (in other words, all the references to the target paper’s citations) and we refer to these extracted papers as the target papers nearest neighbors. For each of the neighboring papers, we qualify candidate papers that are co-cited with the target paper and which has been referenced by at least any of the target papers references. We then measure the degree of similitude between these qualified candidate papers and the target paper by measuring their collaborative similarity using Jaccard similarity measure given by Eq (1) . We then recommend the top-N most comparable papers to the researcher.

research papers on recommender systems

Z 11 Represents the total number of attributes where X and Y both having a value of 1.

Z 01 Represents the total number of attributes where the attribute of X is 0 and the attribute of Y is 1.

Z 10 Represents the total number of attributes where the attribute of X is 1 and the attribute of Y is 0.

To illustrate our approach further, Fig 1 represents a target-paper ( p i ) with references ( Rf 1 to Rf N ) and citations ( Cit . 1 to Cit . N ). Each of the references of the target paper has other citations from any of Rec . 1 to Rec . N and/or Cit . 1 to Cit . N other than the target-paper ( p i ). Also, each of the citations to the target paper has other references from any of Rec . 1 to Rec . N and/or Rf 1 to Rf N other than the target-paper ( p i ). Our approach qualifies recommending papers ( Rec . 1 to Rec . N ) that are co-cited with the target paper and which has been referenced by at least any of the target papers references.


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For example, from Fig 1 , Rec . 1 and Rec . 2 are co-cited with the target paper by Cit . 1 . However, Ref . 2 does not have any connection to any of the target paper’s references and therefore disqualified by step 3 of our proposed algorithm. On the other hand, Rec . 1 does not only being co-cited with the target paper by Cit . 1 but also referenced one of the target papers references Ref . 1 . As can be observed from Fig 1 , only Rec . 1 and Rec . 3 are qualified candidate papers.

In the following section, we present the experiments setup.

4. Experiments setup

4.1 dataset.

We utilize the publicly available dataset presented in [ 2 ]. The dataset contained the publication list of 50 researchers whose research interests are from different fields of computer science that range from information retrieval, software engineering, user interface, security, graphics, databases, operating systems, embedded systems and programming languages. We retrieved every one of their references and citations and extracted from google scholar, every other paper that cited any of the references as well as all the references of each of the target paper’s citations. Some statistics of the utilized dataset is presented in Table 1 .



4.2 Baseline methods

In assessing the effectiveness of our proposed framework, we compare the recommendation results with two baselines presented in [ 7 ] and [ 3 ]. The pattern introduced in [ 7 ] views citation relation matrix as a rating score and generates the recommendation based on common citations between the target paper and its neighboring papers. Given a target paper, the algorithm counts the number of times other citations were co-cited with it. The algorithm then recommends citations with the highest total co-citations summed over all recommending papers. The assumption was that, the more the co-citation in like manner between papers the higher their similarity. While [ 3 ], mined the hidden relationship between a target paper and all of its references. The task was to quantify the degree of closeness between the target paper and the other papers that also cited any of the target paper’s references. The rationale behind the approach was that, if two papers are significantly co-occurring with the same citing paper(s), then they should be similar to some extent.

4.3 Evaluation metrics

In order to evaluate the quality of our approach, for each of the target papers, we performed 5-fold cross validation to its references and citations by selecting 20% as a test set. We then assess the general performance using the three most commonly used evaluation metrics in retrieval systems: precision, recall and F1 measures. Precision given by Eq (2) , measures the capability of the system to reclaim as much relevant research papers as possible in response to the target paper request.

research papers on recommender systems

On the other hand, recall given by Eq (3) , measures the capability of the system to reclaim as few irrelevant research papers as possible in response to the target paper request.

research papers on recommender systems

Moreover, F1 measure given by Eq (4) is the harmonic mean between the precision and recall.

research papers on recommender systems

As users often scan only documents presented at the top ranked of the recommendation list, we feel imperative to also measure the system’s ability to provide useful recommendations at the top of the recommendation list using the two most widely used ranked information retrieval evaluation measures: Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR).

research papers on recommender systems

5. Results and discussions

To be specific, the results of each evaluation metric in this section represent the overall averages over all the 50 researchers of our dataset. We start the comparison by assessing the general performance of our proposed approach in returning relevant research papers with the baseline methods based on the three most commonly used information retrieval evaluation metrics. Figs 2 – 4 , demonstrate the comparisons based on precision, recall and F1 evaluation measures respectively. As can be seen from Fig 2 , the precision results of our proposed approach has significantly outperformed the baseline methods (Context-Based Collaborative Filtering (CCF) proposed by [ 3 ] and Co-citation method proposed by [ 7 ]) in returning relevant research papers for all N recommendations values. This is because our approach is able to critically remove recommending papers that are less related to the target paper.







Fig 3 depicts the comparison based on recall. As can be seen from the figure, the performance difference between our proposed approach and CCF is very much insignificant. In fact, the CCF method is even slightly better than our proposed approach when N = 5 and when N = 20. However, our proposed approach began to show the significant difference as the number of N increases, specifically when N is above 20. The low performance based on recall of our proposed approach is as a result of strict rules in qualifying a candidate paper. Thus, our approach is only after the most significant related recommending papers to the target paper and therefore leaving a lot of other less related papers unrecalled. Furthermore, Fig 4 depicts the harmonic mean between the precision and recall ( F 1 measure), and from the figure, the performance difference between our proposed approach and CCF is also insignificant for values of N less than or equals to 20. However, our approach began to show significant improvement over CCF when N is greater than 20. In all the three measures, the Co-citation method performs very low compared to our proposed approach. This is because the Co-citation method does not infer the hidden associations between paper-citation relations rather applies direct relations between a target paper and its neighboring papers.

Conclusively, the general performance of our proposed approach has outstandingly outperformed the baseline methods based on precision for all values of N . On the other hand, our proposed approach performs worse than CCF in a recommendation list of 5 based on recall and F1 performance measures. However, the major reason behind the low performance of our proposed approach based on recall is the strict rules in qualifying a candidate paper.

Our proposed approach is designed to favor precision which has more influence on user satisfaction than recall. This is because precision is the key element in the process of implementing a search solution [ 55 ]. Poor precision damages the reputation of a search system and discourages its use. High precision generally impresses search users [ 55 ]. That is why our proposed approach is only after the most significant related recommending papers to the target paper (the result of this can easily be seen from Fig 2 ), and therefore leaving a lot of other less related papers unrecalled. This is because recall is particularly important in applications where the user cannot afford to miss information such as issues related to security or compliance applications. The recall has less influence on user satisfaction than precision. Many searchers, especially on the Web, are satisfied by precise results, even where recall is low [ 56 ]. Notwithstanding, our proposed approach starts to show large disparities with the baseline methods when the number of N is above 5 for both recall and F 1 measures. Therefore, a very large N value is extremely important in order to recall as much qualitative and useful recommendations as possible.

Due to the fact that users usually scan only the top of the recommendation list, we also make the comparison based on how our approach is able to return relevant research papers at the top of the recommendation list. Figs 5 and 6 depict our comparisons based on Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) respectively. As can be seen from Fig 5 , that our proposed method has significantly outperformed the baseline methods based on mean average precision (MAP) in all cases in returning the relevant recommendations at the top of the recommendation list. Moreover, the comparison based on mean reciprocal rank (MRR) depicted by Fig 6 has also revealed that our proposed approach has outstandingly outperformed the baseline methods in all scenarios. It can easily be seen from the figure that our approach is able to return a relevant research paper at either rank 1 or rank 2 of the recommendation list for all queries.





As we have pointed out earlier, all these improvements are largely due to the strictness in qualifying a candidate paper which removed less relevant papers to the target paper. This, therefore, increases the system’s ability to return relevant and useful recommendations at the top of the recommendation list.

6. Conclusion and future work

In this paper, we utilized the publicly available contextual metadata to leverage the advantages of collaborative filtering approach in recommending a set of related papers to a researcher based on paper-citation relations. The approach mined the hidden associations between a research paper and its references and citations using paper-citation relations. The rationale behind the approach is that, if two papers are significantly co-occurring with the same citing paper(s), then they should be similar to some extent.

As demonstrated using a publicly available dataset, our proposed method outperforms the baseline methods in measuring both the overall performance and the ability to return relevant and useful research papers at the top of the recommendation list. Based on the three most commonly used information retrieval system metrics, our proposed approach have significantly improved the baseline methods based on precision, recall and F1 measures. Our proposed approach has also recorded significant improvements over the baseline methods in providing relevant and useful recommendations at the top of the recommendation list based on mean average precision (MAP) and mean reciprocal rank (MRR).

In addition to considering the collaborative relations among research papers, our next line of research is to also put into cognizance the public contextual contents, such as titles and abstracts of the recommending papers for better performances.

Supporting information

S1 dataset. the detail of the complete dataset can be accessed via https://figshare.com/articles/supporting_information_dataset_docx/5368408 ..


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A Systematic Study on the Recommender Systems in the E-Commerce

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Title: algorithmic collusion or competition: the role of platforms' recommender systems.

Abstract: Recent academic research has extensively examined algorithmic collusion resulting from the utilization of artificial intelligence (AI)-based dynamic pricing algorithms. Nevertheless, e-commerce platforms employ recommendation algorithms to allocate exposure to various products, and this important aspect has been largely overlooked in previous studies on algorithmic collusion. Our study bridges this important gap in the literature and examines how recommendation algorithms can determine the competitive or collusive dynamics of AI-based pricing algorithms. Specifically, two commonly deployed recommendation algorithms are examined: (i) a recommender system that aims to maximize the sellers' total profit (profit-based recommender system) and (ii) a recommender system that aims to maximize the demand for products sold on the platform (demand-based recommender system). We construct a repeated game framework that incorporates both pricing algorithms adopted by sellers and the platform's recommender system. Subsequently, we conduct experiments to observe price dynamics and ascertain the final equilibrium. Experimental results reveal that a profit-based recommender system intensifies algorithmic collusion among sellers due to its congruence with sellers' profit-maximizing objectives. Conversely, a demand-based recommender system fosters price competition among sellers and results in a lower price, owing to its misalignment with sellers' goals. Extended analyses suggest the robustness of our findings in various market scenarios. Overall, we highlight the importance of platforms' recommender systems in delineating the competitive structure of the digital marketplace, providing important insights for market participants and corresponding policymakers.

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Research paper recommender system based on public contextual metadata

  • Published: 05 August 2020
  • Volume 125 , pages 101–114, ( 2020 )

Cite this article

  • Khalid Haruna   ORCID: orcid.org/0000-0001-6706-8438 1 ,
  • Maizatul Akmar Ismail 2 ,
  • Atika Qazi 3 ,
  • Habeebah Adamu Kakudi 1 ,
  • Mohammed Hassan 4 ,
  • Sanah Abdullahi Muaz 4 &
  • Haruna Chiroma 5  

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Due to the exponential increase in research papers on a daily basis, finding and accessing related academic documents over the Internet is monotonous. One of the leading approaches was the use of recommendation systems to proactively recommend scholarly papers to individual researchers. The primary drawback to these methods, however, is that their success depends on user profile information and is therefore unable to provide useful suggestions to the new user. In addition, both the public and the non-public used descriptive metadata are used. The scope of the recommendation is therefore limited to a number of documents which are either publicly available or which are granted copyright permits. In alleviating the above problems, we proposed an alternative approach using public contextual metadata for an independent framework that customizes scholarly papers, regardless of the research field and user expertise. Experimental tests have shown significant improvements over other baseline methods.

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This study was supported by the Tertiary Education Trust Fund (TETFund) Institutional Based Research (IBR) Fund, through the Directorate of Research, Innovation and Partnership (DRIP) of Bayero University, Kano, Nigeria (2019), and partly supported by University of Malaya under research grant IIRG001B-19SAH.

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Department of Computer Science, Faculty of Computer Science and Information Technology, Bayero University, Kano, Nigeria

Khalid Haruna & Habeebah Adamu Kakudi

Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

Maizatul Akmar Ismail

Centre for Lifelong Learning, Universiti Brunei Darussalam, Gadong, BE1410, Brunei

Department of Software Engineering, Faculty of Computer Science and Information Technology, Bayero University, Kano, Nigeria

Mohammed Hassan & Sanah Abdullahi Muaz

Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Yunlin, Taiwan

Haruna Chiroma

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Correspondence to Khalid Haruna , Maizatul Akmar Ismail or Haruna Chiroma .

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Haruna, K., Ismail, M.A., Qazi, A. et al. Research paper recommender system based on public contextual metadata. Scientometrics 125 , 101–114 (2020). https://doi.org/10.1007/s11192-020-03642-y

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Received : 11 September 2019

Published : 05 August 2020

Issue Date : October 2020

DOI : https://doi.org/10.1007/s11192-020-03642-y

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