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A hybrid personality-aware recommendation system based on personality traits and types models

Journal of Ambient Intelligence and Humanized Computing (2022)Cite this article

Abstract

Personality-aware recommendation systems have been proven to achieve high accuracy compared to conventional recommendation systems. In addition to that, personality-aware recommendation systems could help alleviate cold start and data sparsity problems by adding the user’s personality traits in the recommendation process. The majority of the literature works used Big-Five personality model to represent the user’s personality, this is due to the popularity of Big-Five model in the literature of psychology. However, from personality computing perspective, the choice of the most suitable personality model that satisfy the requirements of the recommendation application and the recommended content type still needs further investigation. In this paper, we study and compare four personality-aware recommendation systems based on different personality models, namely Big-Five traits model, Eysenck model and HEXACO model from the personality traits theory, and Myers–Briggs Type Indicator (MPTI) from the personality types theory. Furthermore, we propose a hybrid personality model for recommendation that takes advantage of the personality traits models, as well as the personality types models. Through extensive experiments on recommendation dataset, we prove the efficiency of the proposed model, especially in cold start settings. Our proposed hybrid personality-aware recommendation model improves the precision and recall in cold start settings by 21% and 18% respectively compared to the widely used Big-Five traits model.

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Data availability statement

Newsfullness dataset is available for researchers by contacting the creator [8], the requester must be affiliated with research institute (e.g. university/research center), and institutional email must be used to request the dataset.

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Funding

This research was funded by National Natural Science Foundation of China, Grant no [61872038].

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Authors and Affiliations

  1. School of Computer Science, University College Dublin, Dublin, Ireland

    Sahraoui Dhelim & Nyothiri Aung

  2. School of Computing, Ulster University, Belfast, UK

    Liming Chen

  3. School of Information Science and Engineering, Linyi University, Linyi, China

    Wenyin Zhang

  4. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Huansheng Ning

Authors
  1. Sahraoui Dhelim
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  2. Liming Chen
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  3. Nyothiri Aung
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  4. Wenyin Zhang
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  5. Huansheng Ning
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Correspondence to Huansheng Ning.

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Dhelim, S., Chen, L., Aung, N. et al. A hybrid personality-aware recommendation system based on personality traits and types models. J Ambient Intell Human Comput (2022). https://doi.org/10.1007/s12652-022-04200-5

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  • DOI: https://doi.org/10.1007/s12652-022-04200-5

Keywords

  • Personality computing
  • Personality-aware recommendation systems
  • Big-Five
  • FFM
  • Recommendation systems
  • MPTI
  • HEXACO
  • Eysenck
  • Social computing