Comparing LDA and LSA Topic Models for Content-Based Movie Recommendation Systems

We propose a plot-based recommendation system, which is based upon an evaluation of similarity between the plot of a video that was watched by a user and a large amount of plots stored in a movie database. Our system is independent from the number of user ratings, thus it is able to propose famous and beloved movies as well as old or unheard movies/programs that are still strongly related to the content of the video the user has watched. The system implements and compares the two Topic Models, Latent Semantic Allocation (LSA) and Latent Dirichlet Allocation (LDA), on a movie database of two hundred thousand plots that has been constructed by integrating different movie databases in a local NoSQL (MongoDB) DBMS. The topic models behaviour has been examined on the basis of standard metrics and user evaluations, performance ssessments with 30 users to compare our tool with a commercial system have been conducted.