Movie Recommendation using YouTube Movie Trailer Data as the Side Information

The user feedback data such as likes, dislikes, comments on movie trailers posted on YouTube can be a useful information source for movie recommender systems. In this paper, we study the effect of adding the feedback data on trailers as a type of the side information to the movie rating data. We propose a recommendation framework that can integrate the trailer and rating data adopting different integration strategies: integrating all the trailer data as movie features, using sentiment scores derived from the trailer comments as a rating matrix to integrate with the movie rating matrix and treating others as the movie features, or only integrating the sentiment score based rating matrix with the movie rating matrix. Our experiment shows that if we include the movie trailer data, recommendation accuracy is improved. We also find that the most accurate result is achieved if all the trailer feedback data is integrated as movie features. To design our system, we use both Matrix Factorization (MF) and Deep Neural Network (DNN) Models. We find that the DNN model performs better than the MF model.