Comparing Topic Models for a Movie Recommendation System

Abstract

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. 1 http://www.netflixprize.com/ 2 The grand prize was given to the BellKor's Pragmatic Chaos team which bested Netflix's own algorithm for predicting ratings by 10.06%.

Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset

“…The scope of assigning a news article to a crime category can be addressed following several approaches, such as text classification, community or topic detection [12][13][14][15]. In this work, we model this problem as a text classification task which consists of automatically assigning text documents to one of the predefined categories.…”