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Technol."],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:p>Representation learning is essential for deep-neural-network-based recommender systems to capture user preferences and item features within fixed-dimensional user and item vectors. Unlike existing representation learning methods that either treat each user preference and item feature uniformly or categorize them into discrete clusters, we argue that in the real world, user preferences and item features are naturally expressed and organized in a hierarchical manner, leading to a new direction for representation learning.<\/jats:p>\n                  <jats:p>\n                    In this article, we introduce a novel\n                    <jats:bold>matryoshka representation learning method for recommendation (MRL4Rec)<\/jats:bold>\n                    , by which we restructure user and item vectors into matryoshka representations with nested vector spaces to explicitly represent user preferences and item features at different hierarchical layers. We theoretically establish that training with the same triplets for each sliced vector\n                    <jats:italic toggle=\"yes\">cannot<\/jats:italic>\n                    guarantee representation learning with hierarchical structures. Subsequently, we propose the\n                    <jats:bold>layer- and hardness-adaptive negative sampling (LHANS)<\/jats:bold>\n                    mechanism to construct training triplets, which further ensures the soundness of learned matryoshka representations in capturing hierarchical user preferences and item features. The experiments demonstrate that MRL4Rec can consistently and substantially outperform a number of state-of-the-art competitors on several real-life datasets. Our code is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Riwei-HEU\/MRL\">https:\/\/github.com\/Riwei-HEU\/MRL<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3800942","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T13:23:14Z","timestamp":1773062594000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Matryoshka Representation Learning for Recommendation with Layer- and Hardness-Adaptive Negative Sampling"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9390-8234","authenticated-orcid":false,"given":"Riwei","family":"Lai","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5842-838X","authenticated-orcid":false,"given":"Li","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4435-4017","authenticated-orcid":false,"given":"Weixin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3853-6308","authenticated-orcid":false,"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1470","volume-title":"Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Cai Liwei","year":"2018","unstructured":"Liwei Cai and William Yang Wang. 2018. 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