Personality, User Preferences and Behavior in Recommender systems

  • Raghav Pavan Karumur
  • Tien T. Nguyen
  • Joseph A. Konstan


This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns.


Personality Recommender systems Big-five personality traits User preferences Newcomer retention 



This work was supported by the National Science Foundation grant IIS-1319382. We thank MovieLens users who took the Personality survey. We also thank Max Harper, Isaac Johnson, Daniel Kluver, Vikas Kumar, Colleen Smith, Lana Yarosh, and Qian Zhao of the GroupLens Research lab for their occasional valuable inputs and feedback on this work. We also thank our reviewers for their valuable suggestions.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.5-240, GroupLens Research, Department of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA

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