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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.11018 |
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| _version_ | 1866913604999053312 |
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| author | Li, Yang Liu, Kangbo Wu, Yaoxin Wang, Zhaoxuan Cambria, Erik Wang, Xiaoxu |
| author_facet | Li, Yang Liu, Kangbo Wu, Yaoxin Wang, Zhaoxuan Cambria, Erik Wang, Xiaoxu |
| contents | Bundle recommendations strive to offer users a set of items as a package named bundle, enhancing convenience and contributing to the seller's revenue. While previous approaches have demonstrated notable performance, we argue that they may compromise the ternary relationship among users, items, and bundles. This compromise can result in information loss, ultimately impacting the overall model performance. To address this gap, we develop a unified model for bundle recommendation, termed hypergraph-enhanced dual convolutional neural network (HED). Our approach is characterized by two key aspects. Firstly, we construct a complete hypergraph to capture interaction dynamics among users, items, and bundles. Secondly, we incorporate U-B interaction information to enhance the information representation derived from users and bundle embedding vectors. Extensive experimental results on the Youshu and Netease datasets have demonstrated that HED surpasses state-of-the-art baselines, proving its effectiveness. In addition, various ablation studies and sensitivity analyses revealed the working mechanism and proved our effectiveness. Codes and datasets are available at https://github.com/AAI-Lab/HED |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_11018 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hypergrah-Enhanced Dual Convolutional Network for Bundle Recommendation Li, Yang Liu, Kangbo Wu, Yaoxin Wang, Zhaoxuan Cambria, Erik Wang, Xiaoxu Information Retrieval Artificial Intelligence Bundle recommendations strive to offer users a set of items as a package named bundle, enhancing convenience and contributing to the seller's revenue. While previous approaches have demonstrated notable performance, we argue that they may compromise the ternary relationship among users, items, and bundles. This compromise can result in information loss, ultimately impacting the overall model performance. To address this gap, we develop a unified model for bundle recommendation, termed hypergraph-enhanced dual convolutional neural network (HED). Our approach is characterized by two key aspects. Firstly, we construct a complete hypergraph to capture interaction dynamics among users, items, and bundles. Secondly, we incorporate U-B interaction information to enhance the information representation derived from users and bundle embedding vectors. Extensive experimental results on the Youshu and Netease datasets have demonstrated that HED surpasses state-of-the-art baselines, proving its effectiveness. In addition, various ablation studies and sensitivity analyses revealed the working mechanism and proved our effectiveness. Codes and datasets are available at https://github.com/AAI-Lab/HED |
| title | Hypergrah-Enhanced Dual Convolutional Network for Bundle Recommendation |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2312.11018 |