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Inf. Syst."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>Personalized 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\u2019 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<\/jats:p>","DOI":"10.1145\/3464764","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T04:37:30Z","timestamp":1632803850000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Exploiting Group Information for Personalized Recommendation with Graph Neural Networks"],"prefix":"10.1145","volume":"40","author":[{"given":"Zhiqiang","family":"Tian","sequence":"first","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei, China"}]},{"given":"Yezheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology and National Engineering Laboratory for Big Data Distribution and Exchange Technologies, China"}]},{"given":"Jianshan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology and Key Laboratory of Process Optimization and Intelligent Decision-making, China"}]},{"given":"Yuanchun","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology and Key Laboratory of Process Optimization and Intelligent Decision-making, China"}]},{"given":"Mingyue","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Management, Hefei University of Technology, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_2_1","volume-title":"Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263","author":"van den Berg Rianne","year":"2017","unstructured":"Rianne van den Berg , Thomas N. 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