{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T04:35:41Z","timestamp":1786250141639,"version":"3.56.0"},"reference-count":117,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>Recommender systems can be characterized as software solutions that provide users with convenient access to relevant content. Traditionally, recommender systems research predominantly focuses on developing machine learning algorithms that aim to predict which content is relevant for individual users. In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not sufficient. Instead, multiple and often competing objectives, e.g., long-term vs. short-term goals, have to be considered, leading to a need for more research in multi-objective recommender systems. We can differentiate between several types of such competing goals, including <jats:italic>(i)<\/jats:italic> competing recommendation quality objectives at the individual and aggregate level, <jats:italic>(ii)<\/jats:italic> competing objectives of different involved stakeholders, <jats:italic>(iii)<\/jats:italic> long-term vs. short-term objectives, <jats:italic>(iv)<\/jats:italic> objectives at the user interface level, and <jats:italic>(v)<\/jats:italic> engineering related objectives. In this paper, we review these types of multi-objective recommendation settings and outline open challenges in this area.<jats:xref><jats:sup>1<\/jats:sup><\/jats:xref><\/jats:p>","DOI":"10.3389\/fdata.2023.1157899","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T05:35:02Z","timestamp":1679463302000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["A survey on multi-objective recommender systems"],"prefix":"10.3389","volume":"6","author":[{"given":"Dietmar","family":"Jannach","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Himan","family":"Abdollahpouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s11257-019-09256-1","article-title":"Multistakeholder recommendation: survey and research directions","volume":"30","author":"Abdollahpouri","year":"2020","journal-title":"User Model Useradapt Interact"},{"key":"B2","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/978-1-0716-2197-4_17","article-title":"\u201cMultistakeholder recommender systems,\u201d","volume-title":"Recommender Systems Handbook","author":"Abdollahpouri","year":"2022"},{"key":"B3","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1145\/3109859.3109912","article-title":"\u201cControlling popularity bias in learning-to-rank recommendation,\u201d","volume-title":"Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys '17","author":"Abdollahpouri","year":"2017"},{"key":"B4","first-page":"413","article-title":"\u201cManaging popularity bias in recommender systems with personalized re-ranking,\u201d","volume-title":"Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS '19)","author":"Abdollahpouri","year":""},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1907.13286","article-title":"The unfairness of popularity bias in recommendation","author":"Abdollahpouri","year":"","journal-title":"arXiv preprint"},{"key":"B6","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1145\/3450613.3456821","article-title":"\u201cUser-centered evaluation of popularity bias in recommender systems,\u201d","volume-title":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021","author":"Abdollahpouri","year":"2021"},{"key":"B7","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1145\/3109859.3109954","article-title":"\u201cRecsys challenge 2017: offline and online evaluation,\u201d","volume-title":"Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017","author":"Abel","year":"2017"},{"key":"B8","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/MIS.2007.58","article-title":"New recommendation techniques for multicriteria rating systems","volume":"22","author":"Adomavicius","year":"2007","journal-title":"IEEE Intell. 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