Comparing Topic Models for a Movie Recommendation System

Recommendation systems have become successful at suggesting content that are likely to be of interest to the user, however their performance greatly suffers when little information about the users preferences are given. In this paper we propose an automated movie recommendation system based on the similarity of movie: given a target movie selected by the user, the goal of the system is to provide a list of those movies that are most similar to the target one, without knowing any user preferences. The Topic Models of Latent Semantic Allocation (LSA) and Latent Dirichlet Allocation (LDA) have been applied and extensively compared on a movie database of two hundred thousand plots. Experiments are an important part of the paper; we examined the topic models behaviour based on standard metrics and on user evaluations, we have conducted performance assessments with 30 users to compare our approach with a commercial system. The outcome was that the performance of LSA was superior to that of LDA in supporting the selection of similar plots. Even if our system does not outperform commercial systems, it does not rely on human effort, thus it can be ported to any domain where natural language descriptions exist. Since it is independent from the number of user ratings, it is able to suggest famous movies as well as old or unheard movies that are still strongly related to the content of the video the user has watched.