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Personality-aware recommendation systems have been proven to achieve highaccuracy compared to conventional recommendation systems. In addition to that,personality-aware recommendation systems could help alleviate cold start anddata sparsity problems. Most of the existing works use Big-Five personalitymodel to represent the user's personality, this is due to the popularity ofBig-Five model in the literature of psychology. However, from personalitycomputing perspective, the choice of the most suitable personality model thatsatisfy the requirements of the recommendation application and the recommendedcontent type still needs further investigation. In this paper, we study andcompare four personality-aware recommendation systems based on differentpersonality models, namely Big-Five, Eysenck and HEXACO from the personalitytraits theory, and Myers-Briggs Type Indicator (MPTI) from the personalitytypes theory. Following that, we propose a hybrid personality model forrecommendation that takes advantage of the personality traits models, as wellas the personality types models. Through extensive experiments onrecommendation dataset, we prove the efficiency of the proposed model,especially in cold start settings.

Big-Five, MPTI, Eysenck or HEXACO: The Ideal Personality Model for Personality-aware Recommendation Systems