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Personality and Recommendation Diversity

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Emotions and Personality in Personalized Services

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Diversity is increasingly recognized as an important metric for evaluating the effectiveness of online recommendations. However, few studies have fully explored the possibility of realizing personalized diversity in recommender systems by taking into account the individual user’s spontaneous needs. In this chapter, we emphasize the effect of users’ personality on their needs for recommendation diversity . We start with a review of the two branches of research in this area, diversity -oriented recommender systems (RS) and personality -based RS. We then report the results from a user survey that we conducted with the aim of identifying the relationship between personality and users’ preferences for recommendation diversity . For instance, the personality trait of conscientiousness can affect users’ preferences not only for diversity in respect of a particular attribute (such as movie genre, country, or release time), but also their preference for overall diversity when all attributes are considered. Motivated by the survey findings, we propose a personality -based diversity -adjusting strategy for recommender systems , and demonstrate its significant merit in improving users’ subjective perceptions of the system’s recommendation accuracy. Finally, we consider implications and suggestions for future research directions.

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Acknowledgments

This study work was supported by the Hong Kong Research Grants Council (no. ECS/HKBU211912) and the China National Natural Science Foundation (no. 61272365).

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Authors and Affiliations

  1. Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Kowloon, Hong Kong

    Li Chen & Wen Wu

  2. East China Normal University, 500 Dongchuan Road, Minhang District, Shanghai, China

    Liang He

Authors
  1. Li Chen
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  2. Wen Wu
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  3. Liang He
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Corresponding author

Correspondence to Wen Wu .

Editor information

Editors and Affiliations

  1. Department of Computational Perception, Johannes Kepler University, Linz, Austria

    Marko Tkalčič

  2. Department of Computer Science, University of Bari Aldo Moro, Bari, Italy

    Berardina De Carolis

  3. Department of Computer Science, University of Bari Aldo Moro, Bari, Italy

    Marco de Gemmis

  4. Preserje, Ljubljana, Slovenia

    Ante Odić

  5. Faculty of electrical engineering, University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

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Chen, L., Wu, W., He, L. (2016). Personality and Recommendation Diversity. In: Tkalčič, M., De Carolis, B., de Gemmis, M., Odić, A., Košir, A. (eds) Emotions and Personality in Personalized Services. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-31413-6_11

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  • DOI: https://doi.org/10.1007/978-3-319-31413-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31411-2

  • Online ISBN: 978-3-319-31413-6

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