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Inf. Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>\n            In recommender systems, click behaviors play a fundamental role in mining users\u2019 interests and training models (clicked items as positive samples). Such signals are\n            <jats:italic>implicit<\/jats:italic>\n            feedback and are arguably less representative of users\u2019 inherent interests. Most existing works denoise implicit feedback by introducing external signals, such as gaze, dwell time, and \u201clike\u201d behaviors. However, such\n            <jats:italic>explicit<\/jats:italic>\n            feedback is not always routinely available, or might be problematic to collect on a large scale. In this paper, we identify that an interaction\u2019s related structural patterns in its neighborhood graph are potentially correlated with some outcome of implicit feedback (i.e., users\u2019 ratings after consuming items), analogous to findings in other domains such as social networks. Inspired by this finding, we propose a novel Structure LEarning based Denoising (SLED) framework for denoising recommendation without explicit signals, which consists of two phases:\n            <jats:italic>center-aware graph structure learning<\/jats:italic>\n            and\n            <jats:italic>denoised recommendation<\/jats:italic>\n            . Phase 1 pre-trains a structural encoder in a self-supervised manner and learns to capture an interaction\u2019s related structural patterns in its neighborhood graph. Phase 2 transfers the structure encoder to downstream recommendation datasets, which helps to down-weight the effect of noisy interactions on user interest modeling and loss calculation. We collect a relatively noisy industrial dataset across several days during a period of product promotion festival. Extensive experiments on this dataset and multiple public datasets demonstrate that the proposed SLED framework can significantly improve the recommendation quality over various base recommendation models.\n          <\/jats:p>","DOI":"10.1145\/3611385","type":"journal-article","created":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T08:52:24Z","timestamp":1691225544000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["SLED: Structure Learning based Denoising for Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-8289","authenticated-orcid":false,"given":"Shengyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhejiang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1656-5070","authenticated-orcid":false,"given":"Tan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7024-9790","authenticated-orcid":false,"given":"Kun","family":"Kuang","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5828-9842","authenticated-orcid":false,"given":"Fuli","family":"Feng","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5995-2217","authenticated-orcid":false,"given":"Jin","family":"Yu","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4614-5320","authenticated-orcid":false,"given":"Jianxin","family":"Ma","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6121-0384","authenticated-orcid":false,"given":"Zhou","family":"Zhao","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1831-0106","authenticated-orcid":false,"given":"Jianke","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0580-9728","authenticated-orcid":false,"given":"Hongxia","family":"Yang","sequence":"additional","affiliation":[{"name":"Alibaba Group, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6097-7807","authenticated-orcid":false,"given":"Tat-Seng","family":"Chua","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2139-8807","authenticated-orcid":false,"given":"Fei","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-8733(03)00009-1"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3430028"},{"key":"e_1_3_2_4_2","article-title":"Beyond node embedding: A direct unsupervised edge representation framework for homogeneous networks","volume":"1912","author":"Bandyopadhyay Sambaran","year":"2019","unstructured":"Sambaran Bandyopadhyay, Anirban Biswas, M. 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