Tree-like Interaction Learning for Bundle Recommendation
Paper
Paper/Presentation Title | Tree-like Interaction Learning for Bundle Recommendation |
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Presentation Type | Paper |
Authors | Ke, Haole, Li, Lin, Wang, Peipei, Yuan, Jingling and Tao, Xiaohui |
Journal or Proceedings Title | Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'23) |
Number of Pages | 5 |
Year | 2023 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICASSP49357.2023.10096246 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/10096246 |
Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/10094559/proceeding |
Conference/Event | 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'23) |
Event Details | 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'23) Parent IEEE International Conference on Acoustics, Speech and Signal Processing Delivery In person Event Date 04 to end of 10 Jun 2023 Event Location Rhodes Island, Greece Event Venue https://2023.ieeeicassp.org/ |
Abstract | Bundle recommendation suggests a set of items to users against their complex needs, where user-bundle interaction learning is key. It is observed that Gromov’s δ-hyperbolicity of the interaction graph in bundle recommendation is smaller (lower is more hyperbolic) than those in traditional item recommendation when measuring a graph’s tree likeness. However, state-of-the-art bundle recommendation methods learn to embed the entities (user, bundle, item) of tree-like interaction graph in Euclidean space, which could cause severe distortion problems. We argue hyperbolic space provides a promising way to get accurate entity embeddings, with this paper proposing a novel bundle recommendation model. The model learns user preferences via hyperbolic graph convolution, aiming at decreasing the distortion of bundle graph node embeddings. Extensive empirical experiments conducted on two real-world datasets confirm that our model achieves promising performance compared to baseline methods representing state-of-the-art bundle recommendation methods. |
Keywords | bundle recommendation; hyperbolic space; graph convolution network |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Wuhan University of Technology, China |
University of Southern Queensland |
https://research.usq.edu.au/item/z9w73/tree-like-interaction-learning-for-bundle-recommendation
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