A New Approach for Fairness Increment of Consensus-Driven Group Recommender Systems Based on Choquet Integral

It has been witnessed in recent years for the rising of Group recommender systems (GRSs) in most e-commerce and tourism applications like Booking.com, Traveloka.com, Amazon, etc. One of the most concerned problems in GRSs is to guarantee the fairness between users in a group so-called the consensus-driven group recommender system. This paper proposes a new flexible alternative that embeds a fuzzy measure to aggregation operators of consensus process to improve fairness of group recommendation and deals with group member interaction. Choquet integral is used to build a fuzzy measure based on group member interactions and to seek a better fairness recommendation. The empirical results on the benchmark datasets show the incremental advances of the proposal for dealing with group member interactions and the issue of fairness in Consensus-driven GRS.

[1]  Hongzhi Yin,et al.  Overcoming Data Sparsity in Group Recommendation , 2020, IEEE Transactions on Knowledge and Data Engineering.

[2]  Hiep Xuan Huynh,et al.  Recommender Systems Based on Resonance Relationship of Criteria With Choquet Operation , 2020, Int. J. Data Warehous. Min..

[3]  Nava Tintarev,et al.  Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance , 2020, RecSys.

[4]  Elke A. Rundensteiner,et al.  Rank aggregation algorithms for fair consensus , 2020, Proc. VLDB Endow..

[5]  Hiep Xuan Huynh,et al.  Recommender Systems Using Collaborative Tagging , 2020, Int. J. Data Warehous. Min..

[6]  Naomie Salim,et al.  Recommendation system based on deep learning methods: a systematic review and new directions , 2019, Artificial Intelligence Review.

[7]  Wei Wang,et al.  Hierarchy Visualization for Group Recommender Systems , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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

[9]  Yiqun Liu,et al.  Fairness-Aware Group Recommendation with Pareto-Efficiency , 2017, RecSys.

[10]  Nikos Mamoulis,et al.  Fairness in Package-to-Group Recommendations , 2017, WWW.

[11]  Thibaut Lust,et al.  Choquet Integral Versus Weighted Sum in Multicriteria Decision Contexts , 2015, ADT.

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

[13]  Irene Díaz,et al.  On random generation of fuzzy measures , 2013, Fuzzy Sets Syst..

[14]  P. Sørensen,et al.  Evaluation of the ranking probabilities for partial orders based on random linear extensions. , 2003, Chemosphere.

[15]  Hiep Xuan Huynh,et al.  Context-Similarity Collaborative Filtering Recommendation , 2020, IEEE Access.

[16]  Marko Tkalcic,et al.  Evaluating Group Recommender Systems , 2018 .

[17]  Pablo Castells,et al.  Group Recommender Systems: New Perspectives in the Social Web , 2012, Recommender Systems for the Social Web.