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, Le Hoang Son

Source Title: International Journal of Data Warehousing and Mining (IJDWM)18(1)

ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799893684|DOI: 10.4018/IJDWM.290891

MLA

Giap, Cu Nguyen, et al. "A New Approach for Fairness Increment of Consensus-Driven Group Recommender Systems Based on Choquet Integral." IJDWM vol.18, no.1 2022: pp.1-22. http://doi.org/10.4018/IJDWM.290891

APA

Giap, C. N., Son, N. N., Giang, N. L., Chau, H. T., Tuan, T. M., & Son, L. H. (2022). A New Approach for Fairness Increment of Consensus-Driven Group Recommender Systems Based on Choquet Integral. International Journal of Data Warehousing and Mining (IJDWM), 18(1), 1-22. http://doi.org/10.4018/IJDWM.290891

Chicago

Giap, Cu Nguyen, et al. "A New Approach for Fairness Increment of Consensus-Driven Group Recommender Systems Based on Choquet Integral," International Journal of Data Warehousing and Mining (IJDWM) 18, no.1: 1-22. http://doi.org/10.4018/IJDWM.290891

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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.