Hypergraph Attribute Attention Network for Community Recommendation

• Published in: 2023 IEEE International Conference on Data Mining (ICDM) pp. 269 - 278
• Main Authors: Li, Kang, Xi, Wu-Dong, Xing, Xing-Xing, Wang, Chang-Dong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2023
• Subjects: attribute; community recommendation; game; Games; hypergraph; Message passing; Multitasking; Self-supervised learning; Semantics; Task analysis; Training
• ISSN: 2374-8486
• DOI: 10.1109/ICDM58522.2023.00036

In recent years, the gaming industry has flourished. Therefore, game manufacturers need to strive to improve the gaming experience of users in the game. Social recommendation tasks in game scenes have become increasingly important. In this work, we focus on community recommendation scenario. A distinctive feature of game community recommendation is that each member can only belong to one gang for a certain duration, which we refer to as uniqueness of communities. The problem caused by uniqueness is that for users to be recommended, there are no positive samples available for training. The challenge caused by uniqueness is that there are no positive samples available for training when users are recommended. Therefore, the collaborative filtering information between the user and the community is very sparse. Meanwhile, existing methods fail to fully model communities and users based on their features and profiles. To address these problems, we propose Hypergraph Attribute Attention Network (HATT) framework. In order to fully exploit user profiles and similarity between users, we discretize user features into entity nodes and model the heterogeneous relationships between users and communities by hyperedge. We propose a hypergraph attention-based message passing mechanism to capture the high-order relationships and obtain embedding with more semantics. At last, we design contrastive learning paradigms to enhance the model's representation ability and apply a multi task training strategy to train the model. Extensive experiments on two real world game datasets are conducted and the results demonstrate the superiority of our method in community recommendation.