{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T23:55:31Z","timestamp":1767830131196,"version":"3.49.0"},"reference-count":34,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T00:00:00Z","timestamp":1615248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2021,4,30]]},"abstract":"<jats:p>\n            It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for the central tendency of users\u2019 distributions. In this work, we demonstrate that a lack of\n            <jats:italic>flatness<\/jats:italic>\n            in rating distributions is negatively correlated with recommendation performance. We propose a rating transformation model that compensates for skew in the rating distribution as well as its central tendency by converting ratings into percentile values as a pre-processing step before recommendation generation. This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance. We also show that a smoothed version of this transformation can yield more intuitive results for users with very narrow rating distributions. A comprehensive set of experiments, with state-of-the-art recommendation algorithms in four real-world datasets, show improved ranking performance for these percentile transformations.\n          <\/jats:p>","DOI":"10.1145\/3437910","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T17:06:43Z","timestamp":1615309603000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Flatter Is Better"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9938-0212","authenticated-orcid":false,"given":"Masoud","family":"Mansoury","sequence":"first","affiliation":[{"name":"DePaul University, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin","family":"Burke","sequence":"additional","affiliation":[{"name":"University of Colorado, Boulder, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bamshad","family":"Mobasher","sequence":"additional","affiliation":[{"name":"DePaul University, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,3,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507229"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1055709.1055714"},{"key":"e_1_2_1_3_1","volume-title":"Recommender Systems Handbook","author":"Adomavicius Gediminas","unstructured":"Gediminas Adomavicius and Alexander Tuzhilin . 2015. Context-aware recommender systems . In Recommender Systems Handbook . Springer US , 191--226. Gediminas Adomavicius and Alexander Tuzhilin. 2015. Context-aware recommender systems. In Recommender Systems Handbook. Springer US, 191--226."},{"key":"e_1_2_1_4_1","first-page":"67","article-title":"From niches to riches: Anatomy of the long tail. Sloan Manage","volume":"47","author":"Brynjolfsson Erik","year":"2006","unstructured":"Erik Brynjolfsson , Yu Jeffrey Hu , and Michael D. Smith . 2006 . From niches to riches: Anatomy of the long tail. Sloan Manage . Rev. 47 , 4 (2006), 67 -- 71 . Erik Brynjolfsson, Yu Jeffrey Hu, and Michael D. Smith. 2006. From niches to riches: Anatomy of the long tail. Sloan Manage. Rev. 47, 4 (2006), 67--71.","journal-title":"Rev."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864721"},{"key":"e_1_2_1_6_1","volume-title":"Weighted percentile-based context-aware recommender system. 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In Proceedings of the Conference on Fairness, Accountability and Transparency. 172--186 . Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness. In Proceedings of the Conference on Fairness, Accountability and Transparency. 172--186."},{"key":"e_1_2_1_8_1","unstructured":"Yoav Goldberg and Omer Levy. 2014. word2vec explained: Deriving Mikolov et\u00a0al.\u2019s negative-sampling word-embedding method. arXiv:1402.3722. Retrieved from https:\/\/arxiv.org\/abs\/1402.3722.  Yoav Goldberg and Omer Levy. 2014. word2vec explained: Deriving Mikolov et\u00a0al.\u2019s negative-sampling word-embedding method. arXiv:1402.3722. 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