Movie Recommendation System Using Color Psychology Based on Emotions

Emotions are the undeniable reliance of information in bridging human and machine intercommunication. Machines are able to recommend better when they can comprehend an individual's emotions. Producing emotions in the users is conventionally recognised as the fundamental goal of movies. Hence, movie recommendations based on one's emotional trajectory is key as it allows them to map movie recommendations based on their emotional stage. This paper uses color psychology in capturing user emotions. Two criterions including collaborative filtering (CF) and content-based filtering (CBF) are applied in comparing the apparent user interest profile and identifying like minded users with their cross-recommended items. This is achieved using algorithms with the ability of computing the recommended hybrid and detecting prevalent the emotions. As a result, the system seeks efficiency improvement and enhancement of quality of recommendations.