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Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n            The diversified recommendation aims to satisfy a user\u2019s different preferences and hence alleviates the information cocoon problem. Previous methods focus on increasing the sample probability of interacted items in the long-tail category. However, these methods are limited by the scope of the historical interactions of a single user and confront the challenge of inadequate diversity of past interactions and unpredictable potential diverse preferences. Drawing from social cognitive theory, observational learning ability allows humans to imitate others\u2019 behaviors when their experience is insufficient. Inspired by it, in this article, we apply the idea of observational learning to the diversified recommendation and introduce a social\n            <jats:italic toggle=\"yes\">Cog<\/jats:italic>\n            nitive Theory Enhanced\n            <jats:italic toggle=\"yes\">D<\/jats:italic>\n            iversified\n            <jats:italic toggle=\"yes\">R<\/jats:italic>\n            ecommendation (\n            <jats:italic toggle=\"yes\">Cog4DR<\/jats:italic>\n            ) model. Specifically, we design a three-step observational learning pipeline, including attention, purification, and retention, corresponding to the three essential stages of observational learning. The pipeline enables the current user to observe other users who have similar tastes but also engage with unique categories, therefore exploring potential diverse preferences and achieving dual improvements in accuracy and diversity. Experimental results indicate that\n            <jats:italic toggle=\"yes\">Cog4DR<\/jats:italic>\n            outperforms all previous approaches, demonstrating the effectiveness of imitating other users\u2019 behaviors for diversified recommendations.\n          <\/jats:p>","DOI":"10.1145\/3767324","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:22:00Z","timestamp":1757629320000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Social Cognitive Theory Enhanced Diversified Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8952-7666","authenticated-orcid":false,"given":"Zhirui","family":"Deng","sequence":"first","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-948X","authenticated-orcid":false,"given":"Zhicheng","family":"Dou","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9432-3251","authenticated-orcid":false,"given":"Yutao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403345"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380281"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/2832415.2832491"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.52.1.1"},{"key":"e_1_3_2_6_2","first-page":"23","volume-title":"Social Foundations of Thought and Action","author":"Bandura Albert","year":"1986","unstructured":"Albert Bandura. 1986. 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