Abstract
Items (users) in a recommender system inherently exhibit hierarchical structures with respect to interactions. Although explicit hierarchical structures are often missing in real-world recommendation scenarios, recent research shows that exploring implicit hierarchical structures for items (users) would largely benefit recommender systems. In this paper, we model user (item) implicit hierarchical structures to capture user-item relationships at various resolution scales resulting in better preferences customization. Specifically, we propose a U-shaped Graph Convolutional Network-based recommender system, namely UGCN, that adopts a hierarchical encoding-decoding process with a message-passing mechanism to construct user (item) implicit hierarchical structures and capture multi-resolution relationships simultaneously. To verify the effectiveness of the UGCN recommender, we conduct experiments on three public datasets. Results have confirmed that the UGCN recommender achieves overall prediction improvements over state-of-the-art models, simultaneously demonstrating a higher recommendation coverage ratio and better-personalized results.
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Acknowledgment
The first author is funded by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China.
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Yi, P., Cai, X., Li, Z. (2023). A U-Shaped Hierarchical Recommender by Multi-resolution Collaborative Signal Modeling. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_34
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