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

Cu Nguyen Giap, Nguyen Nhu Son, Nguyen Long Giang, Hoang Thi Minh Chau, Tran Manh Tuan and Le Hoang Son
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Cu Nguyen Giap: Graduate University of Science and Technology, Vietnam
Nguyen Nhu Son: Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
Nguyen Long Giang: Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
Hoang Thi Minh Chau: University of Economics Technology for Industries (UNETI), Hanoi, Vietnam
Tran Manh Tuan: Thuyloi University, Hanoi, Vietnam
Le Hoang Son: VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam

International Journal of Data Warehousing and Mining (IJDWM), 2022, vol. 18, issue 1, 1-22

Abstract: 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.

Date: 2022
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