{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T20:46:36Z","timestamp":1770151596229,"version":"3.49.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T00:00:00Z","timestamp":1723248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB1406302"],"award-info":[{"award-number":["2019YFB1406302"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072393"],"award-info":[{"award-number":["62072393"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>As an extension of conventional top-K item recommendation solution, bundle recommendation has aroused increasingly attention. However, because of the extreme sparsity of user-bundle (UB) interactions, the existing top-K item recommendation methods suffer from poor performance when applied to bundle recommendation. While some graph-based approaches have been proposed for bundle recommendation, these approaches primarily leverage the bipartite graph to model the UB interactions, resulting in suboptimal performance. In this paper, a dual hypergraph contrastive learning model is proposed for bundle recommendation. First, we model the direct and indirect UB interactions as hypergraphs to represent the higher-order UB relations. Second, we utilize the hypergraph convolution networks to learn the user and bundle embeddings from the hypergraphs, and improve the learned embeddings through a bidirectional contrastive learning strategy. Finally, we adopt a joint loss that combines the InfoBPR loss supporting multiple negative samples and the contrastive losses to optimize model parameters for prediction. Experiments on the real-world datasets indicate that our model performs better than the state-of-the-art baseline methods.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae056","type":"journal-article","created":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T21:13:56Z","timestamp":1723238036000},"page":"2906-2919","source":"Crossref","is-referenced-by-count":3,"title":["DHCL-BR: Dual Hypergraph Contrastive Learning for Bundle Recommendation"],"prefix":"10.1093","volume":"67","author":[{"given":"Peng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology , Beijing Institute of Technology, Beijing 100081,","place":["China"]}]},{"given":"Zhendong","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology , Beijing Institute of Technology, Beijing 100081,","place":["China"]}]},{"given":"Ru","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering , Yanshan University, Qinhuangdao, Hebei Province 066004,","place":["China"]}]},{"given":"Fuzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering , Yanshan University, Qinhuangdao, Hebei Province 066004,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,8,10]]},"reference":[{"key":"2024101809311091600_ref1","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2023.103353","article-title":"Meta-relation assisted knowledge-aware coupled graph neural network for recommendation","volume":"60","author":"Chang","year":"2023","journal-title":"Inf Process Manag"},{"key":"2024101809311091600_ref2","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108618","article-title":"Exploiting high-order local and global user\u2013item interactions for effective recommendation","volume":"246","author":"Tian","year":"2022","journal-title":"Knowl-Based 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