Proceedings

User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 1624-1630.

https://doi.org/10.24963/ijcai.2021/224

Keywords

  • Humans and AI: Personalization and User Modeling
  • Data Mining: Recommender Systems

Abstract

Accurate user modeling is critical for news recommendation. Existing news recommendation methods usually model users' interest from their behaviors via sequential or attentive models. However, they cannot model the rich relatedness between user behaviors, which can provide useful contexts of these behaviors for user interest modeling. In this paper, we propose a novel user modeling approach for news recommendation, which models each user as a personalized heterogeneous graph built from user behaviors to better capture the fine-grained behavior relatedness. In addition, in order to learn user interest embedding from the personalized heterogeneous graph, we propose a novel heterogeneous graph pooling method, which can summarize both node features and graph topology, and be aware of the varied characteristics of different types of nodes. Experiments on large-scale benchmark dataset show the proposed methods can effectively improve the performance of user modeling for news recommendation.

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie. 2021. User-as-Graph: User Modeling with Heterogeneous Graph Pooling for News Recommendation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 1624–1630. https://doi.org/10.24963/ijcai.2021/224
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