User Personality and User Satisfaction with Recommender Systems

Tien T. Nguyen, F. Maxwell Harper, Loren Terveen, Joseph A. Konstan

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

In this study, we show that individual users’ preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual preferences for recommendation diversity, popularity and serendipity by measuring their personality traits. We conducted an online experiment with over 1,800 users for six months on a live recommendation system. In this experiment, we asked users to evaluate a list of movie recommendations with different levels of diversity, popularity, and serendipity. Then, we assessed users’ personality traits using the Ten-item Personality Inventory (TIPI). We found that ratings-based recommender systems may often fail to deliver preferred levels of diversity, popularity, and serendipity for their users (e.g. users with high-serendipity preferences). We also found that users with different personalities have different preferences for these three recommendation properties. Our work suggests that we can improve user satisfaction when we integrate users’ personality traits into the process of generating recommendations.

Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalInformation Systems Frontiers
Volume20
Issue number6
DOIs
StatePublished - Sep 3 2017

Keywords

  • Big-five personality traits
  • Human factors
  • Personality
  • Recommendation diversity
  • Recommendation popularity
  • Recommendation serendipity
  • Recommender systems
  • User preferences

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