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Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias users\u2019 preference ratings after item consumption in the direction of the predicted rating. Top-N lists represent another common approach for presenting item recommendations in recommender systems. Through three controlled laboratory experiments, we show that top-N lists do not induce a discernible bias in user preference judgments. This result is robust, holding for both lists of personalized item recommendations and lists of items that are top-rated based on averages of aggregate user ratings. Adding numerical ratings to the list items does generate a bias, consistent with earlier studies. Thus, in contexts where preference biases are of concern to an online retailer or platform, top-N lists, without numerical predicted ratings, would be a promising format for displaying item recommendations.<\/jats:p>","DOI":"10.1145\/3430028","type":"journal-article","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T11:12:35Z","timestamp":1610622755000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings"],"prefix":"10.1145","volume":"39","author":[{"given":"Gediminas","family":"Adomavicius","sequence":"first","affiliation":[{"name":"University of Minnesota, Minneapolis, Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesse","family":"Bockstedt","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shawn","family":"Curley","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, Minnesota"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Indiana University, Bloomington, Indiana"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,1,14]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"42","volume-title":"Proceedings of the 11th ACM Conference on Recommender Systems. 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