{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T04:17:41Z","timestamp":1784607461490,"version":"3.55.0"},"reference-count":117,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62372399, 62476244"],"award-info":[{"award-number":["62372399, 62476244"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"OPPO Research Fund, and the advanced computing resources provided by the Supercomputing Center of Hangzhou City University"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>Sequential recommenders generate recommendations based on users\u2019 historical interaction sequences. However, in practice, these sequences are often contaminated by noisy interactions, which can arise from various factors such as clickbait, the influence of prominently positioned items, or accidental interactions. Such noise can significantly degrade recommendation performance. Accurately identifying such noisy interactions without additional information is particularly challenging due to the absence of explicit supervisory signals indicating noise. Large Language Models (LLMs), equipped with extensive open knowledge and semantic reasoning abilities, offer a promising avenue to bridge this information gap. However, employing LLMs for denoising in sequential recommendation presents notable challenges: (1) Direct application of pretrained LLMs may not be competent for the denoising task, frequently generating nonsensical responses; (2) Fine-tuning on the denoising task can partially mitigate the issue of generating nonsensical outputs. However, even after fine-tuning, the reliability of LLM outputs remains questionable, especially given the complexity of the denoising task and the inherent hallucination issue of LLMs.<\/jats:p>\n          <jats:p>\n            To tackle these challenges, we propose LLM4DSR, a tailored approach for denoising sequential recommendation using LLMs. We constructed a self-supervised fine-tuning task to activate LLMs\u2019 capabilities to identify noisy items and suggest replacements. Furthermore, we developed an uncertainty estimation module that ensures only high-confidence responses are utilized for sequence corrections. Remarkably, LLM4DSR is model-agnostic, allowing corrected sequences to be flexibly applied across various recommendation models. To the best of our knowledge, this is the first work that employs LLMs for sequential recommendation denoising while addressing the unique challenges of adapting LLMs to this task. Extensive experiments conducted on three real-world datasets across two noise settings validate the effectiveness of LLM4DSR, demonstrating an average improvement of 12.9% in NDCG@20. The code is available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/WANGBohaO-jpg\/LLM4DSR\">https:\/\/github.com\/WANGBohaO-jpg\/LLM4DSR<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3762182","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T13:51:16Z","timestamp":1756129876000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8264-3182","authenticated-orcid":false,"given":"Bohao","family":"Wang","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9265-9431","authenticated-orcid":false,"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"OPPO Research Institute, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4193-7833","authenticated-orcid":false,"given":"Changwang","family":"Zhang","sequence":"additional","affiliation":[{"name":"OPPO Research Institute, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4752-2629","authenticated-orcid":false,"given":"Jiawei","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1332-322X","authenticated-orcid":false,"given":"Yudi","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3645-1041","authenticated-orcid":false,"given":"Sheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3180-0668","authenticated-orcid":false,"given":"Xingyu","family":"Lou","sequence":"additional","affiliation":[{"name":"OPPO Research Institute, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0481-5341","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"OPPO Research Institute, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3605-5404","authenticated-orcid":false,"given":"Yan","family":"Feng","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6198-7481","authenticated-orcid":false,"given":"Chun","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-4307","authenticated-orcid":false,"given":"Can","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et al. 2023. 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