{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T02:15:14Z","timestamp":1770084914740,"version":"3.49.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"name":"the InnoHK initiative, the Government of the HKSAR, and the Laboratory for AI-Powered Financial Technologies"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>Sequential Recommender Systems (SRSs) predict items of interest for users based on their historical interactions. Two key popular paradigms for SRs are unidirectional Next Item Prediction (NIP) and bidirectional Masked Language Modeling (MLM). NIP performs well in recommendation tasks but is constrained by its reliance on prior information and a rigid temporal order assumption, limiting its ability to capture dynamic personalized preferences. MLM, on the other hand, enhances sequence representation and personalization by leveraging bidirectional context, but its objective is misaligned with recommendation tasks.<\/jats:p>\n                  <jats:p>\n                    To this end, we develop MetaSR, a meta-learning-based SR approach that ingeniously combines both paradigms into a unified framework to enhance personalized sequential recommendation. Specifically, we treat the mask prediction task in MLM as the inner-loop task (i.e., the support set), adjusting mask items dynamically to generate sequences that match individual behavioral patterns to capture personalized user preferences. In the outer loop (i.e., the query set), we adopt the NIP paradigm to directly train MetaSR toward the SR target. The joint training bridges the gap between MLM and SRs tasks, also overcoming NIP\u2019s limitations in capturing dynamic personalized preferences. Furthermore, to improve the overall efficiency, we develop a reinforcement learning-based Adaptive Masked Sequence Selection (AMSS) mechanism, automatically selecting the optimal masking prediction tasks within the meta-learning support set to accelerate the meta-learning and achieve better personalization. Experiments on multiple public benchmark datasets show that MetaSR significantly outperforms existing SRs models. The dataset and codes are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/youhua97\/MetaSR\">MetaSR<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3786346","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:23:22Z","timestamp":1767612202000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging NIP and MLM: A Unified Meta-Learning Framework for Sequential Recommendation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1290-3604","authenticated-orcid":false,"given":"Youhua","family":"Li","sequence":"first","affiliation":[{"name":"School of Business, City University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5802-4121","authenticated-orcid":false,"given":"Ersheng","family":"Ni","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Queensland, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8559-4500","authenticated-orcid":false,"given":"Yihao","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Social Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7043-3960","authenticated-orcid":false,"given":"Sibo","family":"Xu","sequence":"additional","affiliation":[{"name":"Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9545-4778","authenticated-orcid":false,"given":"Tianyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8314-2255","authenticated-orcid":false,"given":"Mingxuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4759-2042","authenticated-orcid":false,"given":"Junchen","family":"Fu","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, United Kingdom of Great Britain and Northern Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6669-2677","authenticated-orcid":false,"given":"Yucheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2266-8027","authenticated-orcid":false,"given":"Yuanqi","family":"He","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4209-5671","authenticated-orcid":false,"given":"Xinyuan","family":"Song","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, Georgia, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7606-1475","authenticated-orcid":false,"given":"Yongxin","family":"Ni","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Junyoung Chung Caglar Gulcehre Kyunghyun Cho and Yoshua Bengio. 2014. 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