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Recomm. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Bundle recommendation approaches offer users a set of related items on a particular topic. The current state-of-the-art (SOTA) method utilizes contrastive learning to learn representations at both the bundle and item levels. However, due to the inherent difference between the bundle-level and item-level preferences, the item-level representations may not receive sufficient information from the bundle affiliations to make accurate predictions. In this article, we propose a novel approach, Enhanced Bundle Recommendation (EBRec), which incorporates two enhanced modules to explore inherent item-level bundle representations. First, we propose to incorporate the bundle-user-item (B-U-I) high-order correlations to explore more collaborative information, thus to enhance the previous bundle representation that solely relies on the bundle-item affiliation information. Second, we further enhance the B-U-I correlations by augmenting the observed user-item interactions with interactions generated from pre-trained models, thus improving the item-level bundle representations. We conduct extensive experiments on three public datasets, and the results justify the effectiveness of our approach as well as the two core modules. Codes and datasets are available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/answermycode\/EBRec\">https:\/\/github.com\/answermycode\/EBRec<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3637067","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T11:41:40Z","timestamp":1702467700000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhancing Item-level Bundle Representation for Bundle Recommendation"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4641-1994","authenticated-orcid":false,"given":"Xiaoyu","family":"Du","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6102-2387","authenticated-orcid":false,"given":"Kun","family":"Qian","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3038-5389","authenticated-orcid":false,"given":"Yunshan","family":"Ma","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2344-6174","authenticated-orcid":false,"given":"Xinguang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"237","volume-title":"Proceedings of the RecSys","author":"Brosh Tzoof Avny","year":"2022","unstructured":"Tzoof Avny Brosh, Amit Livne, Oren Sar Shalom, Bracha Shapira, and Mark Last. 2022. BRUCE: Bundle recommendation using contextualized item embeddings. In Proceedings of the RecSys. ACM, 237\u2013245."},{"key":"e_1_3_1_3_2","unstructured":"Xuheng Cai Chao Huang Lianghao Xia and Xubin Ren. 2023. LightGCL: Simple yet effective graph contrastive learning for recommendation. Retrieved from https:\/\/arxiv.org\/abs\/2302.08191."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080779"},{"issue":"3","key":"e_1_3_1_5_2","first-page":"2326","article-title":"Bundle recommendation and generation with graph neural networks","volume":"35","author":"Chang Jianxin","year":"2023","unstructured":"Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2023. Bundle recommendation and generation with graph neural networks. IEEE Trans. Knowl. Data Eng. 35, 3, 2326\u20132340.","journal-title":"IEEE Trans. Knowl. 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