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Inf. Syst."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>\n            In recommender systems, the patterns of user behaviors (e.g., purchase, click) may vary greatly in different contexts (e.g., time and location). This is because user behavior is jointly determined by two types of factors:\n            <jats:italic>intrinsic factors<\/jats:italic>\n            , which reflect consistent user preference, and\n            <jats:italic>extrinsic factors<\/jats:italic>\n            , which reflect external incentives that may vary in different contexts. Differentiating between intrinsic and extrinsic factors helps learn user behaviors better. However, existing studies have only considered differentiating them from a single, pre-defined context (e.g., time or location), ignoring the fact that a user\u2019s extrinsic factors may be influenced by the interplay of various contexts at the same time. In this article, we propose the intrinsic-extrinsic disentangled recommendation (IEDR) model, a generic framework that differentiates intrinsic from extrinsic factors considering various contexts simultaneously, enabling more accurate differentiation of factors and hence the improvement of recommendation accuracy. IEDR contains a context-invariant contrastive learning component to capture intrinsic factors, and a disentanglement component to extract extrinsic factors under the interplay of various contexts. The two components work together to achieve effective factor learning. Extensive experiments on real-world datasets demonstrate IEDR\u2019s effectiveness in learning disentangled factors and significantly improving recommendation accuracy by up to 4% in NDCG.\n          <\/jats:p>","DOI":"10.1145\/3722553","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T17:44:21Z","timestamp":1742319861000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0553-9163","authenticated-orcid":false,"given":"Yixin","family":"Su","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8562-3327","authenticated-orcid":false,"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5341-378X","authenticated-orcid":false,"given":"Fangquan","family":"Lin","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9444-9228","authenticated-orcid":false,"given":"Cheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0885-0643","authenticated-orcid":false,"given":"Sarah M.","family":"Erfani","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9101-1503","authenticated-orcid":false,"given":"Junhao","family":"Gan","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6065-811X","authenticated-orcid":false,"given":"Yunxiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Laboratory of Advanced Biotechnology, Beijing Institute of Biotechnology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7791-5511","authenticated-orcid":false,"given":"Ruixuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8132-6250","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Linas Baltrunas Karen Church Alexandros Karatzoglou and Nuria Oliver. 2015. Frappe: Understanding the usage and perception of mobile app recommendations in-the-wild. arXiv:1505.03014. Retrieved from http:\/\/arxiv.org\/abs\/1505.03014"},{"key":"e_1_3_2_3_2","first-page":"531","article-title":"Mutual information neural estimation","author":"Belghazi Mohamed Ishmael","year":"2018","unstructured":"Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual information neural estimation. In ICML, 531\u2013540.","journal-title":"ICML"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/1467-937X.00253"},{"key":"e_1_3_2_5_2","article-title":"LightGCL: Simple yet effective graph contrastive learning for recommendation","author":"Cai Xuheng","year":"2023","unstructured":"Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple yet effective graph contrastive learning for recommendation. 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Cl4ctr: A contrastive learning framework for CTR prediction. In WSDM, 805\u2013813.","journal-title":"WSDM"},{"key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1145\/3336191.3371836","article-title":"Time to shop for Valentine\u2019s Day: Shopping occasions and sequential recommendation in E-commerce","author":"Wang Jianling","year":"2020","unstructured":"Jianling Wang, Raphael Louca, Diane Hu, Caitlin Cellier, James Caverlee, and Liangjie Hong. 2020. Time to shop for Valentine\u2019s Day: Shopping occasions and sequential recommendation in E-commerce. In WSDM, 645\u2013653.","journal-title":"WSDM"},{"key":"e_1_3_2_40_2","first-page":"6548","article-title":"MISSRec: Pre-training and transferring multi-modal interest-aware sequence representation for recommendation","author":"Wang Jinpeng","year":"2023","unstructured":"Jinpeng Wang, Ziyun Zeng, Yunxiao Wang, Yuting Wang, Xingyu Lu, Tianxiang Li, Jun Yuan, Rui Zhang, Hai-Tao Zheng, and Shu-Tao Xia. 2023. 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