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Previous studies on sequential recommendations mostly aim to identify users\u2019 main recent interests to optimize the recommendation accuracy; they often neglect the fact that users display multiple interests over extended periods of time, which could be used to improve the diversity of lists of recommended items. Existing work related to diversified recommendation typically assumes that users\u2019 preferences are static and depend on post-processing the candidate list of recommended items. However, those conditions are not suitable when applied to sequential recommendations. We tackle sequential recommendation as a list generation process and propose a unified approach to take accuracy as well as diversity into consideration, called\n            <jats:italic>multi-interest, diversified, sequential recommendation<\/jats:italic>\n            . Particularly, an implicit interest mining module is first used to mine users\u2019 multiple interests, which are reflected in users\u2019 sequential behavior. Then an interest-aware, diversity promoting decoder is designed to produce recommendations that cover those interests. For training, we introduce an interest-aware, diversity promoting loss function that can supervise the model to learn to recommend accurate as well as diversified items. We conduct comprehensive experiments on four public datasets and the results show that our proposal outperforms state-of-the-art methods regarding diversity while producing comparable or better accuracy for sequential recommendation.\n          <\/jats:p>","DOI":"10.1145\/3475768","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T15:31:23Z","timestamp":1631115083000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Multi-interest Diversification for End-to-end Sequential Recommendation"],"prefix":"10.1145","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4470-9655","authenticated-orcid":false,"given":"Wanyu","family":"Chen","sequence":"first","affiliation":[{"name":"National University of Defense Technology, China and University of Amsterdam, Amsterdam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2964-6422","authenticated-orcid":false,"given":"Pengjie","family":"Ren","sequence":"additional","affiliation":[{"name":"Shangdong University, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Cai","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Sun","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maarten","family":"De Rijke","sequence":"additional","affiliation":[{"name":"University of Amsterdam, The Netherlands and Ahold Delhaize, Ahold Delhaize, Zaandam, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-1945-5"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.15"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1498759.1498766"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the 24th International Joint Conference on Artificial Intelligence. 1742\u20131748","author":"Ashkan Azin","year":"2015","unstructured":"Azin Ashkan , Branislav Kveton , Shlomo Berkovsky , and Zheng Wen . 2015 . 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