An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness. (15th April 2020)

• Record Type: Journal Article
• Authors: Xiao, Yang; Pei, Qingqi; Yao, Lina; Yu, Shui; Bai, Lei; Wang, Xianzhi
• Is Part Of: Journal of network and computer applications. Volume 156(2020)
• Publication Date: 2020-04-15
• Subjects: Group recommendation -- User activeness -- Probabilistic model -- Fairness -- Coalition game; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2020.102579
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Compared with individual recommendation, recommending services to a group of users is more complicated because of various users' preference should be considered and introduces new challenging such as fairness, which has never been well studied in current works. In this paper, we propose a novel recommendation scheme called PFGR, which combines a probabilistic model with coalition game strategy, to ensure the accuracy and fairness between groups of users. Given a group of users and a set of services, PFGR models a generative process for service selection in light of several observations: 1) each group is related with several topics; 2) users' decisions on the service selection depends on their expertise, the opinions of members they are familiar with, and group influence; 3) each group contains active users and inactive user, whose activeness contributes to the existence of group. PFGR first estimates the preference of each user on a candidate service via combining user's expertise, inherent connection, and group influence. Then, it determines a group's decision on a service by aggregating the preference of group members using adaptive weights. Finally, PFGR considers users' activeness and employs a strategy based on coalition game to produce a ranked list which is fair to each group member as much as possible. Experimental results on three real-world datasets validate that PFGR can achieve higher Hit Rate and Average Reciprocal Hit Rank than state-of-the-art approaches, which indicates that PFGR attains both the precision and fairness of recommendation. Highlights: Considering the fairness in group recommendation setting. Applying Coalition game theory to model social fairness. Considering social activeness for classifying groups.