Understanding effects of personalized vs. aggregate ratings on user preferences

Gediminas Adomavicius, Jesse C Bockstedt, Shawn Curley, Jingjing Zhang

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations


Prior research has shown that online recommendations have significant influence on consumers' preference ratings and their economic behavior. However, research has not examined the anchoring effects of aggregate user ratings, which are also commonly displayed in online retail settings. This research compares and contrasts the anchoring biases introduced by aggregate ratings on consumers' preferences ratings to those produced by personalized recommendations. Through multiple laboratory experiments, we show that the user preferences can be affected (i.e., distorted) by the displayed average online user ratings in a similar manner as has been shown with personalized recommendations. We further compare the magnitude of anchoring biases by personalized recommendations and aggregate ratings. Our results show that when shown separately, aggregate ratings and personalized recommendations create similar effects on user preferences. When shown together, there is no cumulative increase in the effect, and personalized recommendations tend to dominate the effect on user preferences. We also test these effects using an alternative top-N presentation format. Our results here suggest that top-N lists may be an effective presentation solution that maintains key information provided by recommendations while reducing or eliminating decision biases.

Original languageEnglish (US)
Pages (from-to)14-21
Number of pages8
JournalCEUR Workshop Proceedings
StatePublished - 2016
Event2016 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2016 - Boston, United States
Duration: Sep 16 2016 → …


  • Aggregate ratings
  • Anchoring effects
  • Laboratory experiments
  • Personalized ratings
  • Preference bias
  • Recommender systems

ASJC Scopus subject areas

  • General Computer Science


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