A Group Recommendation Algorithm Based on Dividing Subgroup
Junjie Jia, Si Chen, Tianyue Shang · Advanced Theory and Simulations · 2022
Abstract With the rapid development of Internet technology and social networks, traditional personalized recommendation systems have been unable to provide effective recommendation services for groups composed of multiple users. In order to content the needs of group users, the current group recommendation algorithm mainly uses users’ consensus preferences instead of personal preferences for group recommendation, which can only content the preferences of most users and ignore the preferences of a few cold users. In order to improve the fairness of group recommendation, a group recommendation algorithm based on dividing subgroups is proposed. The algorithm introduces the comprehensive trust between users to divide the group into different subgroups, and obtains the initial subgroup recommendation list according to the average strategy. The PageRank idea is used to set the weight of subgroups and assign the number of recommended items to subgroups to generate the group recommendation list. The experimental results show that the algorithm in this paper can content the user's personalized recommendation needs to the greatest extent under the premise of improving the group fair recommendation service.