Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings

Gediminas Adomavicius, Jesse Bockstedt, Shawn Curley, Jingjing Zhang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Prior research has shown a robust effect of personalized product recommendations on user preference judgments for items. Specifically, the display of system-predicted preference ratings as item recommendations has been shown in multiple studies to bias users' 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.

Original languageEnglish (US)
Article number13
JournalACM Transactions on Information Systems
Issue number2
StatePublished - Mar 1 2021

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© 2021 ACM.


  • Recommender systems
  • decision biases
  • personalization
  • top-N recommendations
  • user preferences


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