{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:23:18Z","timestamp":1776093798561,"version":"3.50.1"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"iNAGO Inc."},{"name":"Ontario Centres of Excellence (OCE) and Mitacs"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            Critiquing\u2014where users propose directional preferences to attribute values\u2014has historically been a highly popular method for conversational recommendation. However, with the growing size of catalogs and item attributes, it becomes increasingly difficult and time-consuming to express all of one\u2019s constraints and preferences in the form of critiquing. It is found to be even more confusing in case of critiquing failures: when the system returns no matching items in response to user critiques. To this end, it would seem important to combine a critiquing-based conversational system with a personalized recommendation component to capture implicit user preferences and thus reduce the user\u2019s burden of providing explicit critiques. To examine the impact of such personalization on critiquing, this article reports on a user study with 228 participants to understand user critiquing behavior for two different recommendation algorithms: (i)\n            <jats:italic>non-personalized<\/jats:italic>\n            , that recommends any item consistent with the user critiques; and (ii)\n            <jats:italic>personalized<\/jats:italic>\n            , which leverages a user\u2019s past preferences on top of user critiques. In the study, we ask users to find a restaurant that they think is the most suitable to a given scenario by critiquing the recommended restaurants at each round of the conversation on the dimensions of price, cuisine, category, and distance. We observe that the\n            <jats:italic>non-personalized<\/jats:italic>\n            recommender leads to more critiquing interactions, more severe critiquing failures, overall more time for users to express their preferences, and longer dialogs to find their item of interest. We also observe that\n            <jats:italic>non-personalized<\/jats:italic>\n            users were less satisfied with the system\u2019s performance. They find its recommendations less relevant, more unexpected, and somewhat equally diverse and surprising than those of\n            <jats:italic>personalized<\/jats:italic>\n            ones. The results of our user study highlight an imperative for further research on the integration of the two complementary components of\n            <jats:italic>personalization<\/jats:italic>\n            and\n            <jats:italic>critiquing<\/jats:italic>\n            to achieve the best overall user experience in future critiquing-based conversational recommender systems.\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3597499","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T12:18:18Z","timestamp":1684412298000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["User Experience and the Role of Personalization in Critiquing-Based Conversational Recommendation"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6129-9582","authenticated-orcid":false,"given":"Arpit","family":"Rana","sequence":"first","affiliation":[{"name":"Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7984-8394","authenticated-orcid":false,"given":"Scott","family":"Sanner","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, ON, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1807-430X","authenticated-orcid":false,"given":"Mohamed Reda","family":"Bouadjenek","sequence":"additional","affiliation":[{"name":"Deakin University, Waurn Ponds, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3044-7238","authenticated-orcid":false,"given":"Ronald","family":"Di Carlantonio","sequence":"additional","affiliation":[{"name":"iNAGO Inc. Toronto, ON, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6576-2229","authenticated-orcid":false,"given":"Gary","family":"Farmaner","sequence":"additional","affiliation":[{"name":"iNAGO Inc. Toronto, ON, Canada"}]}],"member":"320","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v32i3.2364"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888906000567"},{"key":"e_1_3_3_4_2","first-page":"180","volume-title":"Encyclopedia of Library and Information Systems, vol. 60, no.32","author":"Burke Robin","year":"2000","unstructured":"Robin Burke. 2000. Knowledge-based recommender systems. In Encyclopedia of Library and Information Systems, vol. 60, no.32. 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