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They allow users to express intent, preferences, constraints, and contexts in a richer fashion, often using natural language (including faceted search and dialogue). Yet more research is needed to find the most effective ways to use this feedback. One challenge is\n            <jats:italic>inferring a user\u2019s semantic intent<\/jats:italic>\n            from the open-ended terms or attributes often used to describe a desired item. This is critical for recommender systems that wish to support users in their everyday, intuitive use of natural language to refine recommendation results. Leveraging\n            <jats:italic>concept activation vectors (CAVs)<\/jats:italic>\n            [\n            <jats:xref ref-type=\"bibr\">26<\/jats:xref>\n            ], a recently developed approach for model interpretability in machine learning, we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in recommender systems. One novel feature of our approach is its ability to distinguish objective and\n            <jats:italic>subjective<\/jats:italic>\n            attributes (both subjectivity of\n            <jats:italic>degree<\/jats:italic>\n            and of\n            <jats:italic>sense<\/jats:italic>\n            ) and associate\n            <jats:italic>different senses<\/jats:italic>\n            of subjective attributes with different users. We demonstrate on both synthetic and real-world datasets that our CAV representation not only accurately interprets users\u2019 subjective semantics but also can be used to improve recommendations through\n            <jats:italic>interactive item critiquing<\/jats:italic>\n            .\n          <\/jats:p>","DOI":"10.1145\/3658675","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T15:46:56Z","timestamp":1713282416000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2517-4907","authenticated-orcid":false,"given":"Christina","family":"G\u00f6pfert","sequence":"first","affiliation":[{"name":"Amazon.com Inc, Berlin, Germany and Bielefeld University, Bielefeld, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4421-7620","authenticated-orcid":false,"given":"Alex","family":"Haig","sequence":"additional","affiliation":[{"name":"Google Inc, Mountain View, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9929-9951","authenticated-orcid":false,"given":"Chih-Wei","family":"Hsu","sequence":"additional","affiliation":[{"name":"Google Inc, Mountain View, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7533-8300","authenticated-orcid":false,"given":"Yinlam","family":"Chow","sequence":"additional","affiliation":[{"name":"Google Inc, Mountain View, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0825-4841","authenticated-orcid":false,"given":"Ivan","family":"Vendrov","sequence":"additional","affiliation":[{"name":"Google Inc, Mountain View, United States and Anthropic, San Francisco, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7433-8421","authenticated-orcid":false,"given":"Tyler","family":"Lu","sequence":"additional","affiliation":[{"name":"Meta AI, Menlo Park, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5412-6133","authenticated-orcid":false,"given":"Deepak","family":"Ramachandran","sequence":"additional","affiliation":[{"name":"Google Inc, Mountain View, United States"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7216-6301","authenticated-orcid":false,"given":"Hubert","family":"Pham","sequence":"additional","affiliation":[{"name":"Google Inc, Mountain View, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0930-8688","authenticated-orcid":false,"given":"Mohammad","family":"Ghavamzadeh","sequence":"additional","affiliation":[{"name":"Amazon.com Inc, Seattle, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9330-4545","authenticated-orcid":false,"given":"Craig","family":"Boutilier","sequence":"additional","affiliation":[{"name":"Google Inc, Mountain View, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"issue":"6","key":"e_1_3_4_2_2","article-title":"Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius Gediminas","year":"2005","unstructured":"Gediminas Adomavicius and Alexander Tuzhilin. 2005. 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