Several approaches have been suggested for providing users with recommendations using their rating history, most of these approaches suffer from new user problem (cold-start) which is the initial lack of items rating. Most hybrid approaches use the combination of the rating based similarity measure with demographic filtering to solve the cold-start problem. In this paper we combined the rating based similarity calculation with the personality traits based similarity and demographic filtering.

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... integrated personality traits with the demographic characteristics because users with the same demographic attributes may have different personality or behavior so personality information is a necessary filtering condition for users to find better reference users to be recommendation guides. Even though users don't have a rating history the proposed enhanced similarity measure will find users with the same demographic attributes like age, gender, location...etc and the same personality traits. This helps to find the k-nearest neighbors for new users and solve cold-start problem which also can improve the performance of the recommender system.