HyPeRM: A hybrid personality-aware recommender for movie - Balakrishnan, Vimala; Arabi, Hossein (Faculty of Computer Science and Information Technology, University of Malaya, 2018)
Recommendation systems aim to provide end users with suggestions about items, social elements, products or services that are likely to be of their interests. Most studies on recommender systems focus on finding ways to improve the recommendations, including personalizing the systems based on details such as demographics, location, time and emotion, among others. In this work, a hybrid recommender system, namely HyPeRM, is presented, which uses users' personality traits along with their demographic details (i.e. age and gender) to improve the overall quality of recommendations. The popular Big Five personality trait measurement scale was used to gauge users' personalities. HyPeRM was evaluated using two metrics, that is, Standardized Root Mean Square Residual (SRMR) and Root Mean Square Error of Approximation (RMSEA). Both metrics revealed that HyPeRM outperformed the baseline model (i.e. one without user's personality) in terms of the recommendation accuracies. The study shows that user recommendations can be further enhanced when their personality traits are taken into consideration, and thus their overall search experience can be improved as well.