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Data"],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>The information era brings both opportunities and challenges to information services. Confronting information overload, recommendation technology is dedicated to filtering personalized content to meet users\u2019 requirements. The extremely sparse interaction records and their imbalanced distribution become a big obstacle to building a high-quality recommendation model. In this article, we propose a swarm self-supervised hypergraph embedding (SHE) model to predict users\u2019 interests by hypergraph convolution and self-supervised discrimination. SHE builds a hypergraph with multiple interest clues to alleviate the interaction sparsity issue and performs interest propagation to embed CF signals in hybrid learning on the hypergraph. It follows an auxiliary local view by similar hypergraph construction and interest propagation to restrain unnecessary propagation between user swarms. Besides, interest contrast further inserts self-discrimination to deal with long-tail bias issue and enhance interest modeling, which aid recommendation by a multi-task learning optimization. Experiments on public datasets show that the proposed SHE outperforms the state-of-the-art models demonstrating the effectiveness of hypergraph-based interest propagation and swarm-aware interest contrast to enhance embedding for recommendation.<\/jats:p>","DOI":"10.1145\/3638058","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T12:01:21Z","timestamp":1703073681000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Swarm Self-supervised Hypergraph Embedding for Recommendation"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5659-5128","authenticated-orcid":false,"given":"Meng","family":"Jian","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7380-3875","authenticated-orcid":false,"given":"Yulong","family":"Bai","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9645-2246","authenticated-orcid":false,"given":"Jingjing","family":"Guo","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7209-0215","authenticated-orcid":false,"given":"Lifang","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,13]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Proceedings of the ACM Conference on Recommender System.","author":"Abdollahpouri Himan","year":"2019","unstructured":"Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. 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