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Personality, User Preferences and Behavior in Recommender systems

Author

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  • Raghav Pavan Karumur

    (University of Minnesota)

  • Tien T. Nguyen

    (University of Minnesota)

  • Joseph A. Konstan

    (University of Minnesota)

Abstract

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.

Suggested Citation

  • Raghav Pavan Karumur & Tien T. Nguyen & Joseph A. Konstan, 2018. "Personality, User Preferences and Behavior in Recommender systems," Information Systems Frontiers, Springer, vol. 20(6), pages 1241-1265, December.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:6:d:10.1007_s10796-017-9800-0
    DOI: 10.1007/s10796-017-9800-0
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    References listed on IDEAS

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    1. Cobb-Clark, Deborah A. & Schurer, Stefanie, 2012. "The stability of big-five personality traits," Economics Letters, Elsevier, vol. 115(1), pages 11-15.
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    4. Wenge Rong & Baolin Peng & Yuanxin Ouyang & Kecheng Liu & Zhang Xiong, 2015. "Collaborative personal profiling for web service ranking and recommendation," Information Systems Frontiers, Springer, vol. 17(6), pages 1265-1282, December.
    5. Vinodh Krishnaraju & Saji K Mathew & Vijayan Sugumaran, 2016. "Web personalization for user acceptance of technology: An empirical investigation of E-government services," Information Systems Frontiers, Springer, vol. 18(3), pages 579-595, June.
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    Cited by:

    1. Anastasia Griva & Cleopatra Bardaki & Katerina Pramatari & Georgios Doukidis, 2022. "Factors Affecting Customer Analytics: Evidence from Three Retail Cases," Information Systems Frontiers, Springer, vol. 24(2), pages 493-516, April.
    2. 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.
    3. Srivastava, Abhishek & Bala, Pradip Kumar & Kumar, Bipul, 2020. "New perspectives on gray sheep behavior in E-commerce recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    4. 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.
    5. Mohammad Alamgir Hossain & Shams Rahman, 2021. "Investigating the Success of OGB in China: The Influence of Personality Traits," Information Systems Frontiers, Springer, vol. 23(3), pages 543-559, June.
    6. Supavich Fone Pengnate & Rathindra Sarathy & Todd J. Arnold, 2021. "The Influence of the Centrality of Visual Website Aesthetics on Online User Responses: Measure Development and Empirical Investigation," Information Systems Frontiers, Springer, vol. 23(2), pages 435-452, April.
    7. 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.

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