A Survey on Bundle Recommendation: Methods, Applications, and Challenges
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915816862121984 |
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| author | Sun, Meng Li, Lin Li, Ming Tao, Xiaohui Zhang, Dong Xie, Qing Wang, Peipei Huang, Jimmy Xiangji |
| author_facet | Sun, Meng Li, Lin Li, Ming Tao, Xiaohui Zhang, Dong Xie, Qing Wang, Peipei Huang, Jimmy Xiangji |
| contents | In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items. This survey provides a comprehensive review on bundle recommendation, beginning by a taxonomy for exploring product bundling. We classify it into two categories based on bundling strategy from various application domains, i.e., discriminative and generative bundle recommendation. Then we formulate the corresponding tasks of the two categories and systematically review their methods: 1) representation learning from bundle and item levels and interaction modeling for discriminative bundle recommendation; 2) representation learning from item level and bundle generation for generative bundle recommendation. Subsequently, we survey the resources of bundle recommendation including datasets and evaluation metrics, and conduct reproducibility experiments on mainstream models. Lastly, we discuss the main challenges and highlight the promising future directions in the field of bundle recommendation, aiming to serve as a useful resource for researchers and practitioners. Our code and datasets are publicly available at https://github.com/WUT-IDEA/bundle-recommendation-survey. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00341 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Bundle Recommendation: Methods, Applications, and Challenges Sun, Meng Li, Lin Li, Ming Tao, Xiaohui Zhang, Dong Xie, Qing Wang, Peipei Huang, Jimmy Xiangji Information Retrieval In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items. This survey provides a comprehensive review on bundle recommendation, beginning by a taxonomy for exploring product bundling. We classify it into two categories based on bundling strategy from various application domains, i.e., discriminative and generative bundle recommendation. Then we formulate the corresponding tasks of the two categories and systematically review their methods: 1) representation learning from bundle and item levels and interaction modeling for discriminative bundle recommendation; 2) representation learning from item level and bundle generation for generative bundle recommendation. Subsequently, we survey the resources of bundle recommendation including datasets and evaluation metrics, and conduct reproducibility experiments on mainstream models. Lastly, we discuss the main challenges and highlight the promising future directions in the field of bundle recommendation, aiming to serve as a useful resource for researchers and practitioners. Our code and datasets are publicly available at https://github.com/WUT-IDEA/bundle-recommendation-survey. |
| title | A Survey on Bundle Recommendation: Methods, Applications, and Challenges |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2411.00341 |