{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:13:44Z","timestamp":1788232424859,"version":"build-2803163510"},"reference-count":165,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n                    Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason\n                    <jats:italic>why<\/jats:italic>\n                    an individual user accesses the service may change from visit to visit or even during an ongoing usage session. To be effective, a recommender system should therefore aim to take the users\u2019 probable\n                    <jats:italic>intent<\/jats:italic>\n                    of using the service at a certain point in time into account. In recent years, researchers have thus started to address this challenge by incorporating\n                    <jats:italic>intent-awareness<\/jats:italic>\n                    into recommender systems. Correspondingly, a number of technical approaches were put forward, including diversification techniques, intent prediction models, or latent intent modeling approaches. In this article, we survey and categorize existing approaches to building the next generation of\n                    <jats:italic>Intent-Aware Recommender Systems<\/jats:italic>\n                    (IARS). Based on an analysis of current evaluation practices, we outline open gaps and possible future directions in this area, which in particular include the consideration of additional interaction signals and contextual information to further improve the effectiveness of such systems.\n                  <\/jats:p>","DOI":"10.1145\/3700890","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:13:29Z","timestamp":1729062809000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["A Survey on Intent-aware Recommender Systems"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4698-8507","authenticated-orcid":false,"given":"Dietmar","family":"Jannach","sequence":"first","affiliation":[{"name":"University of Klagenfurt, Klagenfurt, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4805-5516","authenticated-orcid":false,"given":"Markus","family":"Zanker","sequence":"additional","affiliation":[{"name":"Free University of Bozen-Bolzano, Bolzano, Italy and University of Klagenfurt, Klagenfurt, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s11257-019-09256-1","article-title":"Multistakeholder recommendation: Survey and research directions","volume":"30","author":"Abdollahpouri Himan","year":"2020","unstructured":"Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020. 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