Automatic Movie Tag Generation System for Improving the Recommendation System

Abstract

As the content industry develops, the demand for movie content is increasing. Accordingly, the content industry is actively developing super-personalized recommendation systems that match consumers’ tastes. In this paper, we study automatic generation of movie tags to improve the movie recommendation system. We extracted background sounds from movie trailer videos, analyzed the sounds using STFT (Short-Time Fourier transform) and major audio attribute features, and created a genre prediction model. The experimental results show that the pre-collected dataset and the data extracted via the model are similar when compared. In this research, we suggest the methodology of an automatic genre prediction system for movie information from trailer videos. This will help to reduce the time and effort for metadata generation for a recommendation system.