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MovieOcean: Assessment of a Personality-based Recommender System

Abstract:

This research effort explores the incorporation of personality treats into user-user collaborative filtering algorithms. To explore the performance of such a method, MovieOcean, a movie recommender system that uses a questionnaire based on the Big Five model to generate personality profiles, was implemented. These personality profiles are used to precompute personality-based neighborhoods, which are then used to pbkp_redict movie ratings and generate recommendations. In an offline analysis, the root mean square error metric is computed to analyze the accuracy of the pbkp_redicted ratings and the F1-score to assess the relevance of the recommendations for the personality-based and a standard-rating-based approach. The obtained results showed that the root mean square error of the personality-based recommender system improves when the personality has a higher weight than the information about the user ratings. A subsequent t-test was conducted for the proposed personality-based approach underperformed based on the root mean square error metric. Furthermore, interviews with users suggested that including aspects of personality when computing recommendations is well-perceived and can indeed help improve current recommendation methods.