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User Personality and User Satisfaction with Recommender Systems

Author

Listed:
  • Tien T. Nguyen

    (University of Minnesota)

  • F. Maxwell Harper

    (University of Minnesota)

  • Loren Terveen

    (University of Minnesota)

  • Joseph A. Konstan

    (University of Minnesota)

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.

Suggested Citation

  • Tien T. Nguyen & F. Maxwell Harper & Loren Terveen & Joseph A. Konstan, 2018. "User Personality and User Satisfaction with Recommender Systems," Information Systems Frontiers, Springer, vol. 20(6), pages 1173-1189, December.
  • Handle: RePEc:spr:infosf:v:20:y:2018:i:6:d:10.1007_s10796-017-9782-y
    DOI: 10.1007/s10796-017-9782-y
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    Cited by:

    1. Eleanna Kafeza & Christos Makris & Gerasimos Rompolas & Feras Al-Obeidat, 2021. "Behavioral and Migration Analysis of the Dynamic Customer Relationships on Twitter," Information Systems Frontiers, Springer, vol. 23(5), pages 1303-1316, September.
    2. Narjes Vara & Mahdieh Mirzabeigi & Hajar Sotudeh & Seyed Mostafa Fakhrahmad, 2022. "Application of k-means clustering algorithm to improve effectiveness of the results recommended by journal recommender system," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3237-3252, June.
    3. Ludovico Boratto & Salvatore Carta & Andreas Kaltenbrunner & Matteo Manca, 2018. "Guest Editorial: Behavioral-Data Mining in Information Systems and the Big Data Era," Information Systems Frontiers, Springer, vol. 20(6), pages 1153-1156, December.
    4. Eleanna Kafeza & Christos Makris & Gerasimos Rompolas & Feras Al-Obeidat, 0. "Behavioral and Migration Analysis of the Dynamic Customer Relationships on Twitter," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    5. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2022. "Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems," Information Systems Frontiers, Springer, vol. 24(1), pages 267-286, February.
    6. Ransome Epie Bawack & Emilie Bonhoure, 2023. "Influencer is the New Recommender: insights for Theorising Social Recommender Systems," Information Systems Frontiers, Springer, vol. 25(1), pages 183-197, February.
    7. Xiongfei Cao & Ali Nawaz Khan & Ahsan Ali & Naseer Abbas Khan, 2020. "Consequences of Cyberbullying and Social Overload while Using SNSs: A Study of Users’ Discontinuous Usage Behavior in SNSs," Information Systems Frontiers, Springer, vol. 22(6), pages 1343-1356, December.
    8. Muh‐Chyun Tang & I‐Han Liao, 2022. "Preference diversity and openness to novelty: Scales construction from the perspective of movie recommendation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(9), pages 1222-1235, September.
    9. Kwak, Kyu Tae & Lee, Seung Yeop & Lee, Sang Woo, 2021. "News and user characteristics used by personalized algorithms: The case of Korea's news aggregators, Naver News and Kakao News," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    10. Xiongfei Cao & Ali Nawaz Khan & Ahsan Ali & Naseer Abbas Khan, 0. "Consequences of Cyberbullying and Social Overload while Using SNSs: A Study of Users’ Discontinuous Usage Behavior in SNSs," Information Systems Frontiers, Springer, vol. 0, pages 1-14.

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