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Inf. Syst."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Sequential recommendation methods are crucial in modern recommender systems for their remarkable capability to understand a user\u2019s changing interests based on past interactions. However, a significant challenge faced by current methods (e.g., RNN- or Transformer-based models) is to effectively and efficiently capture users\u2019 preferences by modeling long behavior sequences, which impedes their various applications like short video platforms where user interactions are numerous. Recently, an emerging architecture named\n                    <jats:italic toggle=\"yes\">Mamba<\/jats:italic>\n                    , built on state space models (SSM) with efficient hardware-aware designs, has showcased the tremendous potential for sequence modeling, presenting a compelling avenue for addressing the challenge effectively. Inspired by this, we propose a novel generic and efficient framework (\n                    <jats:italic toggle=\"yes\">SSD4Rec<\/jats:italic>\n                    ) for sequential recommendations, which explores the seamless adaptation of Mamba for recommendations. Specifically, SSD4Rec marks the long-length item sequences with sequence registers and processes the item representations with a novel Masked Bidirectional Structured State Space Duality block. This not only allows for hardware-aware matrix multiplication but also empowers outstanding capabilities in variable-length and long-range sequence modeling. Extensive evaluations on four benchmark datasets demonstrate that the proposed model achieves state-of-the-art performance while maintaining near-linear scalability with user sequence length. Our implementation based on PyTorch is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ZhangYifeng1995\/SSD4Rec\">https:\/\/github.com\/ZhangYifeng1995\/SSD4Rec<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3773038","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T13:45:30Z","timestamp":1762350330000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["SSD4Rec: A Structured State Space Duality Model for Efficient Sequential Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1702-0327","authenticated-orcid":false,"given":"Yifeng","family":"Zhang","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7129-8586","authenticated-orcid":false,"given":"Haohao","family":"Qu","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6903-8996","authenticated-orcid":false,"given":"Liangbo","family":"Ning","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-1233","authenticated-orcid":false,"given":"Wenqi","family":"Fan","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-471X","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159668"},{"key":"e_1_3_3_5_2","first-page":"10041","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Dao Tri","year":"2024","unstructured":"Tri Dao and Albert Gu. 2024. Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality. In Proceedings of the International Conference on Machine Learning. PMLR, 10041\u201310071."},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109877"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01056-9"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531985"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3008732"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599575"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3426723"},{"key":"e_1_3_3_13_2","first-page":"1","volume-title":"Proceedings of the 1st Conference on Language Modeling","author":"Gu Albert","year":"2024","unstructured":"Albert Gu and Tri Dao. 2024. Mamba: Linear-time sequence modeling with selective state spaces. In Proceedings of the 1st Conference on Language Modeling, 1\u201332."},{"key":"e_1_3_3_14_2","first-page":"1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Gu Albert","year":"2021","unstructured":"Albert Gu, Karan Goel, and Christopher Re. 2021. Efficiently modeling long sequences with structured state spaces. In Proceedings of the International Conference on Learning Representations, 1\u201332."},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330839"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109882"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0030"},{"key":"e_1_3_3_18_2","unstructured":"Wei He Kai Han Yehui Tang Chengcheng Wang Yujie Yang Tianyu Guo and Yunhe Wang. 2024. 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