Disentangled Dynamic Graph Attention Network for Out-of-Distribution Sequential Recommendation

作者：Zeyang Zhang, Xin Wang, Haibo Chen, Haoyang Li, Wenwu Zhu · 发表于：ACM Transactions on Information Systems · 年份：2024 · DOI：10.1145/3701988 · 被引用次数：14 · 研究领域：Recommender Systems and Techniques、Advanced Graph Neural Networks、Topic Modeling

Sequential recommendation, leveraging user-item interaction histories to provide personalized and timely suggestions, has drawn significant research interest recently. With the power of exploiting spatio-temporal dynamics, Dynamic Graph Neural Networks (DyGNNs) show great potential in sequential recommendation by modeling the dynamic relationship between users and items. However, spatio-temporal distribution shifts naturally exist in out-of-distribution sequential recommendation, where both user-item relationships and temporal sequences demonstrate pattern shifts. The out-of-distribution scenarios may lead to the failure of existing DyGNNs in handling spatio-temporal distribution shifts in sequential recommendation, given that the patterns they exploit tend to be variant w.r.t labels under distribution shifts. In this article, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion ( I-DIDA ) to handle spatio-temporal distribution shifts in sequential recommendation by discovering and utilizing invariant patterns , i.e., structures and features whose predictive abilities are stable across distribution shifts. Specifically, we first propose a disentangled spatio-temporal attention network to capture the variant and invariant patterns. By utilizing the disentangled patterns, we design a spatio-temporal intervention mechanism to create multiple interventional distributions and an environment inference module to infer the latent spati...