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

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.