Matrix Factorization+ for Movie Recommendation

We present a novel model for movie recommendations using additional visual features extracted from pictural data like posters and still frames, to better understand movies. In particular, several context-based methods for recommendation are shown to be special cases of our proposed framework. Unlike existing context-based approaches, our method can be used to incorporate visual features – features that are lacking in existing contextbased approaches for movie recommendations. In reality, movie posters and still frames provide us with rich knowledge for understanding movies as well as users’ preferences. For instance, user may want to watch a movie at the minute when she/he finds some released posters or still frames attractive. Unfortunately, such unique features cannot be revealed from rating data or other forms of context being used in most of existing methods. In this paper, we take a step forward in this direction and investigate both low-level and high-level visual features from the movie posters and still frames for further improvement of recommendation methods. Extensive experiments on real world datasets show that our approach leads to significant improvement over several state-of-the-art methods.