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Inf. Syst."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Conversational Recommendation Systems (CRS) effectively address information asymmetry by dynamically eliciting user preferences through multi-turn interactions. However, existing CRS methods commonly assume that users have clear, definite preferences for one or multiple target items. This assumption can lead to over-trusting user feedback, treating accepts\/rejects as definitive signals to filter items and reduce the candidate space, potentially causing over-filtering and excluding relevant alternatives. In reality, users often exhibit vague preferences, lacking well-defined inclinations for certain attribute types (e.g., color, pattern), and their decision-making process during interactions is rarely binary. Instead, users\u2019 choices are relative, reflecting a range of preferences rather than strict likes or dislikes. To address this issue, we introduce a novel scenario called Vague Preference Multi-Round Conversational Recommendation (VPMCR), which employs a soft estimation mechanism to assign non-zero confidence scores to all candidate items, accommodating users\u2019 vague and dynamic preferences while mitigating over-filtering.<\/jats:p>\n          <jats:p>In the VPMCR setting, we introduce a solution called Vague Preference Policy Learning (VPPL), which consists of two main components: Ambiguity-Aware Soft Estimation (ASE) and Dynamism-Aware Policy Learning (DPL). ASE aims to accommodate the ambiguity in user preferences by estimating preference scores for both directed and inferred preferences, employing a choice-based approach and a time-aware preference decay strategy. DPL implements a policy learning framework, leveraging the preference distribution from ASE, to guide the conversation and adapt to changes in users\u2019 preferences for making recommendations or querying attributes.<\/jats:p>\n          <jats:p>Extensive experiments conducted on diverse datasets demonstrate the effectiveness of VPPL within the VPMCR framework, outperforming existing methods and setting a new benchmark for CRS research. Our work represents a significant advancement in accommodating the inherent ambiguity and relative decision-making processes exhibited by users, improving the overall performance and applicability of CRS in real-world settings.<\/jats:p>","DOI":"10.1145\/3717831","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T15:04:51Z","timestamp":1740063891000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Vague Preference Policy Learning for Conversational Recommendation"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4092-993X","authenticated-orcid":false,"given":"Gangyi","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5187-9196","authenticated-orcid":false,"given":"Chongming","family":"Gao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1704-2260","authenticated-orcid":false,"given":"Wenqiang","family":"Lei","sequence":"additional","affiliation":[{"name":"Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1946-1179","authenticated-orcid":false,"given":"Xiaojie","family":"Guo","sequence":"additional","affiliation":[{"name":"IBM Thomas J Watson Research Center, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4495-0732","authenticated-orcid":false,"given":"Shijun","family":"Li","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, Austin, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9164-2898","authenticated-orcid":false,"given":"Hongshen","family":"Chen","sequence":"additional","affiliation":[{"name":"JD. com Inc, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3082-5198","authenticated-orcid":false,"given":"Zhuozhi","family":"Ding","sequence":"additional","affiliation":[{"name":"JD. com Inc, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0345-334X","authenticated-orcid":false,"given":"Sulong","family":"Xu","sequence":"additional","affiliation":[{"name":"JD. com Inc, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8081-6275","authenticated-orcid":false,"given":"Lingfei","family":"Wu","sequence":"additional","affiliation":[{"name":"Anytime.AI, New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543846"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1086\/209535"},{"key":"e_1_3_2_4_2","first-page":"2787","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garc\u00eda-Dur\u00e1n, Jason Weston, and Oksana Yakhnenko. 2013. 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