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dc.contributor.authorXie, Yueqi
dc.contributor.authorGao, Jingqi
dc.contributor.authorZhou, Peilin
dc.contributor.authorYe, Qichen
dc.contributor.authorHua, Yining
dc.contributor.authorKim, Jae Boum
dc.contributor.authorWu, Fangzhao
dc.contributor.authorKim, Sunghun
dc.date.accessioned2023-10-03T15:14:12Z
dc.date.available2023-10-03T15:14:12Z
dc.date.issued2023-09-14
dc.identifier.isbn979-8-4007-0241-9
dc.identifier.urihttps://hdl.handle.net/1721.1/152334
dc.publisherACM|Seventeenth ACM Conference on Recommender Systemsen_US
dc.relation.isversionofhttps://doi.org/10.1145/3604915.3608766en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleRethinking Multi-Interest Learning for Candidate Matching in Recommender Systemsen_US
dc.typeArticleen_US
dc.identifier.citationXie, Yueqi, Gao, Jingqi, Zhou, Peilin, Ye, Qichen, Hua, Yining et al. 2023. "Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2023-10-01T07:48:37Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2023-10-01T07:48:38Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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