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Personalized Hybrid Book Recommender

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  • Hossein Arabi

    (Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia)

  • Vimala Balakrishnan

    (Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia)

Abstract

Personalized Recommendation Systems (RS) provide end users with suggestions about items that are likely to be of their interest based on users' details such as demographics, location, time, and emotion. In this article, a Personalized Hybrid Book Recommender (PHyBR) is presented, which integrates personality traits with users' demographic data and geographical location to improve the quality of recommendations. The Ten Item Personality Inventory (TIPI) was used to determine users' personality traits. PHyBR was evaluated using two metrics, that are, Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA). Both metrics revealed PHyBR outperforms the baseline models (without considering personality traits and geographical location factor) in terms of the recommendation accuracies. This study shows that users who are in the same geographical contexts intend to have similar preferences. Therefore, users' personality details along with their geographical locations can be used to provide improved personalized recommendations.

Suggested Citation

  • Hossein Arabi & Vimala Balakrishnan, 2019. "Personalized Hybrid Book Recommender," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 11(3), pages 70-97, July.
    • Handle: RePEc:igg:jisss0:v:11:y:2019:i:3:p:70-97
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