Content-based group recommender systems: A general taxonomy and further improvements

Abstract Group recommender systems have emerged as a solution to recommend interesting, suitable, and useful items that are consumed socially by groups of people, rather than individually. Such systems have pushed for the use of new recommendation methods within such an emerging scenario, in which the use of the collaborative filtering paradigm is the core of the recommender algorithm. However, collaborative filtering presents several drawbacks and limitations in this scenario, such as the need for lots of rating values, as well as their co-occurrence across several items and users (scarcity). In order to overcome these drawbacks, this research explores a taxonomy for content-based group recommendation systems (CB-GRS), and subsequently the paper discusses and analyzes three specific models that can be used to build CB-GRS, which are (1) CB-GRSs supported by recommendation aggregation and individual ranking, (2) CB-GRSs supported by recommendation aggregation and user-item matching, and (3) CB-GRSs supported by the aggregation of user profiles. Furthermore, the paper presents a hybrid CB-GRS that combines the models (2) and (3) and integrates feature weighting and aggregation function switching. An experimental protocol over well-known datasets is then developed in order to evaluate the proposals. The current study aims at providing a basis to develop a research branch concerning content-based group recommender systems.

[1]  Gaston Crommenlaan.  TravelWithFriends : a Hybrid Group Recommender System for Travel Destinations , 2016 .

[2]  Liliana Ardissono,et al.  Intrigue: Personalized recommendation of tourist attractions for desktop and hand held devices , 2003, Appl. Artif. Intell..

[3]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[4]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[5]  Luis Martínez,et al.  Opinion Dynamics-Based Group Recommender Systems , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  Rosa M. Rodríguez,et al.  Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures , 2014, Int. J. Comput. Intell. Syst..

[7]  Luis Martínez,et al.  A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items , 2019, IEEE Access.

[8]  George Forman,et al.  A Live Comparison of Methods for Personalized Article Recommendation at Forbes.com , 2012, ECML/PKDD.

[9]  Alexander Felfernig,et al.  Group Recommender Systems: An Introduction , 2018 .

[10]  FATIH GEDIKLI,et al.  Improving recommendation accuracy based on item-specific tag preferences , 2013, TIST.

[11]  Octavio Loyola-González,et al.  Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View , 2019, IEEE Access.

[12]  C. Ravindranath Chowdary,et al.  A survey on group recommender systems , 2019, Journal of Intelligent Information Systems.

[13]  Wei Wang,et al.  Recommender system application developments: A survey , 2015, Decis. Support Syst..

[14]  Mehrbakhsh Nilashi,et al.  Collaborative filtering recommender systems , 2013 .

[15]  Raciel Yera,et al.  On group recommendation supported by a minimum cost consensus model , 2018 .

[16]  Luis Martínez-López,et al.  An empirical study of natural noise management in group recommendation systems , 2017, Decis. Support Syst..

[17]  Zehra Cataltepe,et al.  Feature selection for movie recommendation , 2016 .

[18]  Toon De Pessemier,et al.  TravelWithFriends: a hybrid group recommender system for travel destinations , 2015, RecSys 2015.

[19]  Maria Soledad Pera,et al.  Automating readers' advisory to make book recommendations for K-12 readers , 2014, RecSys '14.

[20]  Mehrbakhsh Nilashi,et al.  A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS , 2015, Electron. Commer. Res. Appl..

[21]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[22]  Luis Martínez-López,et al.  A Recommender System for Programming Online Judges Using Fuzzy Information Modeling , 2018, Informatics.

[23]  Luis Martínez,et al.  Managing Natural Noise in Recommender Systems , 2016, TPNC.

[24]  Shou-De Lin,et al.  A Content-Based Matrix Factorization Model for Recipe Recommendation , 2014, PAKDD.

[25]  Guy Shani,et al.  A Survey of Accuracy Evaluation Metrics of Recommendation Tasks , 2009, J. Mach. Learn. Res..

[26]  Francesca Rossi,et al.  A Self-Adaptive Context-Aware Group Recommender System , 2016, AI*IA.

[27]  Tsvi Kuflik,et al.  Workshop on information heterogeneity and fusion in recommender systems (HetRec 2010) , 2010, RecSys '10.

[28]  Luis Martínez-López,et al.  A recommendation approach for programming online judges supported by data preprocessing techniques , 2017, Appl. Intell..

[29]  Thierry Marchant.  The Borda rule and Pareto stability: a further comment , 2001, Fuzzy Sets Syst..

[30]  Mária Bieliková,et al.  Group Recommendations: Survey and Perspectives , 2014, Comput. Informatics.

[31]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[32]  Francesco Ricci,et al.  A chat-based group recommender system for tourism , 2017, Information Technology & Tourism.

[33]  Nahla Ben Amor,et al.  Aggregating Top-K Lists in Group Recommendation Using Borda Rule , 2017, IEA/AIE.

[34]  Arun K. Pujari,et al.  Group Recommender Systems: A Virtual User Approach Based on Precedence Mining , 2013, Australasian Conference on Artificial Intelligence.

[35]  Dilip Singh Sisodia,et al.  Aggregation of preference relations to enhance the ranking quality of collaborative filtering based group recommender system , 2020, Expert Syst. Appl..

[36]  Xiaohua Jia,et al.  Special section: Scalable information systems , 2008, Future Gener. Comput. Syst..

[37]  Doo-Kwon Baik,et al.  An enhanced aggregation method considering deviations for a group recommendation , 2018, Expert Syst. Appl..

[38]  Fernando Ortega,et al.  Recommending items to group of users using Matrix Factorization based Collaborative Filtering , 2016, Inf. Sci..

[39]  Liang He,et al.  Evaluating recommender systems , 2012, Seventh International Conference on Digital Information Management (ICDIM 2012).

[40]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[41]  Luis Martínez,et al.  A fuzzy approach for natural noise management in group recommender systems , 2018, Expert Syst. Appl..

[42]  Toon De Pessemier,et al.  Comparison of group recommendation algorithms , 2014, Multimedia Tools and Applications.

[43]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[44]  Akiko Aizawa,et al.  An information-theoretic perspective of tf-idf measures , 2003, Inf. Process. Manag..

[45]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[46]  Marijn Koolen,et al.  Trends in content-based recommendation , 2019, User Modeling and User-Adapted Interaction.

[47]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[48]  Ahmad A. Alzahrani,et al.  A Food Recommender System Considering Nutritional Information and User Preferences , 2019, IEEE Access.

[49]  Luis Martínez,et al.  Fuzzy Tools in Recommender Systems: A Survey , 2017, Int. J. Comput. Intell. Syst..

[50]  Luis Martínez-López,et al.  Exploring Fuzzy Rating Regularities for Managing Natural Noise in Collaborative Recommendation , 2019, Int. J. Comput. Intell. Syst..

[51]  F. Maxwell Harper,et al.  The MovieLens Datasets: History and Context , 2016, TIIS.

[52]  Hwanjo Yu,et al.  Improving top-K recommendation with truster and trustee relationship in user trust network , 2016, Inf. Sci..

[53]  Xiao Liu,et al.  Group recommendation based on a bidirectional tensor factorization model , 2017, World Wide Web.

[54]  Derek Bridge,et al.  Diversity, Serendipity, Novelty, and Coverage , 2016, ACM Trans. Interact. Intell. Syst..

[55]  Takashi Yagi,et al.  Group recommendation using feature space representing behavioral tendency and power balance among members , 2011, RecSys '11.

[56]  Luis Martínez-López,et al.  Group Recommendations Based on Hesitant Fuzzy Sets , 2018, Int. J. Intell. Syst..

[57]  Domonkos Tikk,et al.  Recommending new movies: even a few ratings are more valuable than metadata , 2009, RecSys '09.

[58]  Mu Zhu,et al.  Content‐boosted matrix factorization techniques for recommender systems , 2012, Stat. Anal. Data Min..

[59]  Francesco Ricci,et al.  Group recommendations with rank aggregation and collaborative filtering , 2010, RecSys '10.

[60]  Sarik Ghazarian,et al.  Enhancing memory-based collaborative filtering for group recommender systems , 2015, Expert Syst. Appl..

[61]  Francesca Rossi,et al.  A Borda count for collective sentiment analysis , 2015, Annals of Mathematics and Artificial Intelligence.

[62]  Juan A. Recio-García,et al.  An architecture and functional description to integrate social behaviour knowledge into group recommender systems , 2014, Applied Intelligence.

[63]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[64]  Luis Martínez-López,et al.  A Consensus‐Driven Group Recommender System , 2015, Int. J. Intell. Syst..

[65]  Yin Zhang,et al.  GroRec: A Group-Centric Intelligent Recommender System Integrating Social, Mobile and Big Data Technologies , 2016, IEEE Transactions on Services Computing.

[66]  Pasquale Lops,et al.  Semantics-aware Content-based Recommender Systems , 2014, CBRecSys@RecSys.

[67]  Gianni Fenu,et al.  Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering , 2016, Future Gener. Comput. Syst..

[68]  Maria Soledad Pera,et al.  A group recommender for movies based on content similarity and popularity , 2013, Inf. Process. Manag..

[69]  Mária Bieliková,et al.  Personalized hybrid recommendation for group of users: Top-N multimedia recommender , 2016, Inf. Process. Manag..

[70]  Gao Cong,et al.  COM: a generative model for group recommendation , 2014, KDD.

[71]  Tevfik Aytekin,et al.  Clustering-based diversity improvement in top-N recommendation , 2013, Journal of Intelligent Information Systems.

[72]  Jugal K. Kalita,et al.  Towards an Unsupervised Method for Network Anomaly Detection in Large Datasets , 2014, Comput. Informatics.

[73]  Wei Wang,et al.  Member contribution-based group recommender system , 2016, Decis. Support Syst..

[74]  Raciel Yera,et al.  An Intelligent System for Sequencing Product Innovation Activities in Hotels , 2019, IEEE Latin America Transactions.

[75]  Arun K. Pujari,et al.  Virtual user approach for group recommender systems using precedence relations , 2015, Inf. Sci..

[76]  Marcos Aurélio Domingues,et al.  Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems , 2013, Inf. Process. Manag..

[77]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[78]  Yehuda Koren,et al.  Advances in Collaborative Filtering , 2011, Recommender Systems Handbook.

[79]  Le Hoang Son.  HU-FCF++: A novel hybrid method for the new user cold-start problem in recommender systems , 2015, Eng. Appl. Artif. Intell..

[80]  Hamed Movahedian,et al.  Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach , 2014, J. Inf. Sci..

[81]  Andreas Holzinger,et al.  Data Mining with Decision Trees: Theory and Applications , 2015, Online Inf. Rev..