Sep 27, 2021 � In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups,
In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups,
The relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs,�...
Mar 3, 2022 � Bibliographic details on Exploiting Group Information for Personalized Recommendation with Graph Neural Networks.
Exploiting Group Information for Personalized Recommendation with Graph Neural Networks - Free download as Word Doc (.doc / .docx), PDF File (.pdf),�...
In this study, we introduce GcPp, a clustering algorithm that leverages pairwise preference data to generate recommendations for user groups.
In this article, we solve these problems with the graph neural networks technique. First, each session is represented as a graph rather than a linear sequence�...
We study this problem in the context recommender systems at Netflix. We observe that there is abundant semantic information such as genre, content maturity�...
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Aug 19, 2024 � 刘业政,lyz,合肥工业大学主页平台管理系统, Exploiting group information for personalized recommendation with graph neural networks刘业政,
Exploiting Group Information for Personalized Recommendation with Graph Neural Networks � ACM Transactions on Information Systems ◽. 10.1145/3464764 ◽. 2022�...