Self-Supervised Hypergraph Convolutional Network for Group Recommendation
Authors: 
Aijing Sun, Dan Ren, Xinru Wang, Kailin YangAuthors Info & Claims
Aijing Sun, Dan Ren, Xinru Wang, Kailin YangAuthors Info & ClaimsPages 683 - 690
Abstract
With the rise of social media, group recommendation has become an increasingly popular way to recommend items to a group of users. Traditional group recommendation methods use predefined strategies for preference aggregation. However, these strategies are too simple, mainly focus on the pairwise connection of users, and cannot simulate the real and complex high-order interactions inside and outside the group. In addition, the contribution of group members to group decision and the weight of users in different groups should not be the same. In order to solve the above problems, this paper proposes a Self-Supervised Hypergraph Convolutional Network for Group Recommendation(HCNGR), which consists of user-level network and group-level network, in which the user-level network encodes the relationship among users’ complex high-order relationships within the group. Group-level networks capture cross-group interaction information through graph convolution operations. To further alleviate the data sparsity problem, this paper integrates self-supervised learning in the model to maximize the mutual information between the group representations learned by the two networks . Our results on two benchmark datasets show that the proposed model consistently outperforms the state-of-the-art group recommendation methods.
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September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
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Published: 14 June 2024
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AIPR 2023
AIPR 2023: 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 22 - 24, 2023
Xiamen, China
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