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