Paper Info
Title
Exploiting Group Information for Personalized Recommendation with Graph Neural Networks
Abstract
AbstractPersonalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared
Year
DOI
Venue
2022
10.1145/3464764
ACM Transactions on Information Systems
Keywords
DocType
Volume
Personalized recommendation, graph neural network, group preferences
Journal
40
Issue
ISSN
Citations 
2
1046-8188
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Zhiqiang Tian100.34
Yezheng Liu214524.69
Jianshan Sun319217.65
Yuanchun Jiang418421.24
Mingyue Zhu500.34