Predicting Movie Popularity and Ratings with Visual Features

In Movie Recommender Systems, when a new user registers to the system and she has not yet provided any information about her, the system may not be able to generate personalized recommendations for that user. In such a Cold Start situation, many real-world recommender systems suggest popular movies to the new user. Such movies are very likely to be interesting to the new users. A very common approach for measuring the movie popularity is based on counting the number of ratings (as user votes) provided by a community of the existing users. However, in certain cases, we cannot properly measure the popularity of the movies with this common approach. This paper proposes a novel method for predicting the popularity of movies. The method is based on hybrid visually-driven features, representative of the movie content, which can be used to effectively predict not only the movie popularity but also the average rating of the movie. Our extensive experiments on a large dataset of more than 13'000 movies trailers show that the proposed hybrid approach achieves promising results by exploiting visual Attractiveness features of movies in comparison to the other baseline features.