Selecting a movie often requires users to perform numerous operations when faced with vast resources from online movie platforms. Personalized recommendation services can effectively solve this problem by using annotating information from users. However, such current services are less accurate than expected because of their lack of comprehensive consideration for annotation. Thus, in this study, we propose a hybrid movie recommendation approach using tags and ratings. We built this model through the following processes. First, we constructed social movie networks and a preference-topic model. Then, we extracted, normalized, and reconditioned the social tags according to user preference based on social content annotation. Finally, we enhanced the recommendation model by using supplementary information based on user historical ratings. This model aims to improve fusion ability by applying the potential effect of two aspects generated by users. One aspect is the personalized scoring system and the singular value decomposition algorithm, the other aspect is the tag annotation system and topic model. Experimental results show that the proposed method significantly outperforms three categories of recommendation approaches, namely, user-based collaborative filtering (CF), model-based CF, and topic model based CF. 
A hybrid approach for movie recommendation via tags and ratings