A Personality-Based Recommender System for Semantic Searches in Vehicles Sales Portals

  • Fábio A. P. Paiva
  • José A. F. Costa
  • Cláudio R. M. Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)

Abstract

This work proposes a personality-based recommender system to implement semantic searches on Internet Vehicles Sales Portals. The system is based on a typical recommender system architecture that has been extended to combine a hybrid recommendation approach with a machine learning classifier technique (k-NN). It proposes a combination of the Five Factor Model (Big Five Model) with a correlation between car fronts and power and sociability perceptions. A prototype was implemented to answer the semantic searches considering personality-based user’s profiles and a set of Brazilian cars. After each search, a questionnaire was provided for the users to verify how successful the recommendations were for them. The prototype received web searches during a period of 15 days. The final report showed that 77.67% of the users accepted the personality-based recommendations, what indicates that the proposed approach could be promising to improve the quality of the recommendations on the user’s point of view.

Keywords

Recommender system Personality traits Semantic searches 

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fábio A. P. Paiva
    • 1
  • José A. F. Costa
    • 2
  • Cláudio R. M. Silva
    • 2
  1. 1.Federal Institute of Rio Grande do Norte, IFRNParnamirimBrazil
  2. 2.Federal University of Rio Grande do Norte, UFRNNatalBrazil

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