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

Nguyen Giang, Nguyen Son, Cu Giap, Hoang Chau, Tran Tuan, Le Son

OAI: oai:igi-global.com:290891 • DOI: 10.4018/IJDWM.290891

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.

Data Mining Computer Science and Information Technology Data Mining and Databases