Mining contextual movie similarity with matrix factorization for context-aware recommendation

Abstract

Context-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. We propose a novel context-aware movie recommendation algorithm based on joint matrix factorization (JMF). We jointly factorize the user-item matrix containing general movie ratings and other contextual movie similarity matrices to integrate contextual information into the recommendation process. The algorithm was developed within the scope of the mood-aware recommendation task that was offered by the Moviepilot mood track of the 2010 context-aware movie recommendation (CAMRa) challenge. Although the algorithm could generalize to other types of contextual information, in this work, we focus on two: movie mood tags and movie plot keywords. Since the objective in this challenge track is to recommend movies for a user given a specified mood, we devise a novel mood-specific movie similarity measure for this purpose. We enhance the recommendation based on this measure by also deploying the second movie similarity measure proposed in this article that takes into account the movie plot keywords. We validate the effectiveness of the proposed JMF algorithm with respect to the recommendation performance by carrying out experiments on the Moviepilot challenge dataset. We demonstrate that exploiting contextual information in JMF leads to significant improvement over several state-of-the-art approaches that generate movie recommendations without using contextual information. We also demonstrate that our proposed mood-specific movie similarity is better suited for the task than the conventional mood-based movie similarity measures. Finally, we show that the enhancement provided by the movie similarity capturing the plot keywords is particularly helpful in improving the recommendation to those users who are significantly more active in rating the movies than other users.