Learning Multi-interest Embedding with Dynamic Graph Cluster for Sequention Recommendation

XiaoChunjing, Guoranhao, Zhang Yongwang, Xiaoming Wu

Keywords: recommender system; sequential recommendation ;multi-interest model; dynamic graph cluster

TL;DR: MDGR is a multi-interest model that dynamically captures relationships between non-adjacent nodes and improves predictions using interest sub-graphs and attention mechanisms.

Abstract:

Multi-interest recommendation is to predict the next item by representing diversity of a user preference with multiple interest embeddings. Although existing methods have achieved convincing results in recommendation tasks, they ignore the continuously changing relations of no-adjacent items in a sequence. In this paper, we focus on how to fully capture the changing relations when capturing the user multi-interest representations. Specifically, we propose a novel dynamic graph cluster-based multi-interest model named MDGR, which not only comprehensively explores the real changing item relations between no-adjacent items by iteratively
constructing and continuously optimizing interest sub-graph to update the multiple interest embeddings but also collaborates temporal information and interest weight to model the interactive behaviors of users and items.Our model iteratively constructs and continuously optimizes the interest sub-graph by comprehensively adopting dynamic graph cluster to explore the item relations in sequences. That is beneficial to dynamically model user multiple interests. Furthermore, we employ the attention module to extract different influence of various interest embeddings. Finally, we use the refined item embedding and the final multi-interest embeddings to retrieval the next item that a user is most likely to interact with. To the best of our knowledge, this is the first attempt to explore multi-interest embeddings by iteratively constructing and continuously optimizing the interest sub-graph. Extensive experiments on three popular benchmark datasets demonstrate that MDGR outperforms several state-of-the-art methods.

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission358/Authors, auai.org/UAI/2025/Conference/Submission358/Reproducibility_Reviewers

Submission Number: 358