Hyperbolic Mutual Learning for Bundle Recommendation
Paper
Ke, Haole, Li, Lin, Wang, PeiPei, Yuan, Jingling and Tao, Xiaohui. 2023. "Hyperbolic Mutual Learning for Bundle Recommendation." 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023). Tianjin, China 17 - 20 Apr 2023 Switzerland . Springer. https://doi.org/10.1007/978-3-031-30672-3_28
Paper/Presentation Title | Hyperbolic Mutual Learning for Bundle Recommendation |
---|---|
Presentation Type | Paper |
Authors | Ke, Haole, Li, Lin, Wang, PeiPei, Yuan, Jingling and Tao, Xiaohui |
Journal or Proceedings Title | Proceedings of 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023) |
Journal Citation | 13944, pp. 417-433 |
Number of Pages | 17 |
Year | 2023 |
Publisher | Springer |
Place of Publication | Switzerland |
ISBN | 9783031306716 |
9783031306723 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-30672-3_28 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-031-30672-3_28 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-031-30672-3 |
Conference/Event | 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023) |
Event Details | 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023) Parent Database Systems for Advanced Applications Delivery In person Event Date 17 to end of 20 Apr 2023 Event Location Tianjin, China |
Abstract | Bundle recommendation aims to accurately predict the probabilities of user interactions with bundles. Most existing effective methods learn the embeddings of users and bundles from user-bundle interaction view and user-item-bundle interaction view. However, they seldom leverage the recommendation difference caused by the distinct learning trends of two views when modeling user preferences. Meanwhile, such two view interaction graphs are typically tree-like. If the graph data with this structure is embedded in Euclidean space, it will lead to severe distortion problem. To this end, we propose a novel Hyperbolic Mutual Learning model for Bundle Recommendation (HyperMBR). The model encodes the entities (user, item, bundle) of the two view interaction graphs in hyperbolic space to learn their accurate representations. Furthermore, a mutual distillation based on hyperbolic distance is proposed to encourage the two views to transfer knowledge for increasingly improving the recommendation performance. Extensive empirical experiments on two real-world datasets confirm that our HyperMBR achieves promising results compared to state-of-the-art bundle recommendation methods. |
Keywords | Bundle recommendation; Mutual Learning; Hyperbolic Space |
ANZSRC Field of Research 2020 | 461105. Reinforcement learning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | Wuhan University of Technology, China |
University of Southern Queensland |
Permalink -
https://research.usq.edu.au/item/z2769/hyperbolic-mutual-learning-for-bundle-recommendation
72
total views2
total downloads3
views this month0
downloads this month