  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. Most of the existing works use 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, Eysenck and HEXACO from the personality
traits theory, and Myers-Briggs Type Indicator (MPTI) from the personality
types theory. Following that, 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.
