Using visual features and latent factors for movie recommendation

Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on attributes such as genres. Traditionally, movie features are human-generated, either editorially or by leveraging the wisdom of the crowd. In this short paper, we present a recommender system for movies based of Factorization Machines that makes use of the low-level visual features extracted automatically from movies as side information. Low-level visual features - such as lighting, colors and motion - represent the design aspects of a movie and characterize its aesthetic and style. Our experiments on a dataset of more than 13K movies show that recommendations based on low-level visual features provides almost 10 times better accuracy in comparison to genre based recommendations, in terms of various evaluation metrics.