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Inf. Syst."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    Existing recommendation methods struggle to model users\u2019 multifaceted preferences due to the diversity and volatility of user behavior, as well as the inherent uncertainty and ambiguity of item themes in practical scenarios. Multi-interest recommendation addresses this challenge by explicitly extracting multiple interest representations from users\u2019 historical interactions, enabling fine-grained preference modeling and more accurate recommendations. It has attracted considerable attention in recommendation research. However, current recommendation surveys have either delved into specific recommendation tasks and downstream applications or focused on approaches that model users and items as single representations with cutting-edge techniques, overlooking users\u2019 diverse preferences and the multifaceted aspects of items. In this work, we systematically review the progress, solutions, challenges, and future directions of multi-interest recommendation by answering the following three questions: (1)\n                    <jats:italic toggle=\"yes\">Why<\/jats:italic>\n                    is multi-interest modeling significantly important for recommendation? (2)\n                    <jats:italic toggle=\"yes\">What<\/jats:italic>\n                    aspects are focused on by multi-interest modeling in recommendation? and (3)\n                    <jats:italic toggle=\"yes\">How<\/jats:italic>\n                    can multi-interest modeling be applied, along with the technical details of the representative modules? We hope that this survey establishes a fundamental framework and delivers a preliminary overview for researchers interested in this field and committed to further exploration. The implementation of multi-interest recommendation summarized in this survey is maintained at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/WHUIR\/Multi-Interest-Recommendation-A-Survey\">https:\/\/github.com\/WHUIR\/Multi-Interest-Recommendation-A-Survey<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3789510","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T15:42:59Z","timestamp":1770738179000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Interest Recommendation: A Survey"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7840-3364","authenticated-orcid":false,"given":"Zihao","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3871-5350","authenticated-orcid":false,"given":"Qiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Tencent WeChat, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6755-871X","authenticated-orcid":false,"given":"Lixin","family":"Zou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0764-4258","authenticated-orcid":false,"given":"Aixin","family":"Sun","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3144-6374","authenticated-orcid":false,"given":"Chenliang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"issue":"6","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius Gediminas","year":"2005","unstructured":"Gediminas Adomavicius and Alexander Tuzhilin. 2005. 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