{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:58:10Z","timestamp":1772557090994,"version":"3.50.1"},"reference-count":127,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"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":[[2025,12,31]]},"abstract":"<jats:p>\n            Novel data sources bring new opportunities to improve the quality of recommender systems and serve as a catalyst for the creation of new paradigms on personalized recommendations. Impressions are a novel data source containing the items shown to users on their screens. Past research focused on providing personalized recommendations using interactions and occasionally using impressions when such a data source was available. Interest in impressions has increased due to their potential to provide more accurate recommendations. Despite this increased interest, research in recommender systems using impressions is still dispersed. Many works have distinct interpretations of impressions and use impressions in recommender systems in numerous different manners. To unify those interpretations into a single framework, we present a systematic literature review on recommender systems using impressions, focusing on three fundamental perspectives:\n            <jats:italic>recommendation models<\/jats:italic>\n            ,\n            <jats:italic>datasets<\/jats:italic>\n            , and\n            <jats:italic>evaluation methodologies<\/jats:italic>\n            . We define a theoretical framework to delimit recommender systems using impressions and a novel paradigm for personalized recommendations, called impression-aware recommender systems. We propose a classification system for recommenders in this paradigm, which we use to categorize the recommendation models, datasets, and evaluation methodologies used in past research. Last, we identify open questions and future directions, highlighting missing aspects in the reviewed literature.\n          <\/jats:p>","DOI":"10.1145\/3712292","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T10:56:22Z","timestamp":1736938582000},"page":"1-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Impression-Aware Recommender Systems"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6578-7404","authenticated-orcid":false,"given":"Fernando Benjam\u00edn","family":"P\u00e9rez Maurera","sequence":"first","affiliation":[{"name":"Politecnico di Milano, Milan, Italy and ContentWise, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7103-2788","authenticated-orcid":false,"given":"Maurizio","family":"Ferrari Dacrema","sequence":"additional","affiliation":[{"name":"Politecnico di Milano, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0668-6317","authenticated-orcid":false,"given":"Pablo","family":"Castells","sequence":"additional","affiliation":[{"name":"Universidad Aut\u00f3noma de Madrid, Madrid, Spain and Amazon.com Inc, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1253-8081","authenticated-orcid":false,"given":"Paolo","family":"Cremonesi","sequence":"additional","affiliation":[{"name":"Politecnico di Milano, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1145\/2959100.2959207","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems (RecSys\u201916)","author":"Abel Fabian","year":"2016","unstructured":"Fabian Abel, Andr\u00e1s A. 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