PERSONALIZED RANKING OF MOVIES: EVALUATING DIFFERENT METADATA TYPES AND RECOMMENDATION STRATEGIES

Resumo

This paper proposes a study and comparison among a variety of metadata types in order to identify the most relevant pieces of information in order to identify the most relevant pieces of information in personalized ranking of movie items. We used four algorithms available in the literature to analyze the descriptions, and compared each other using the metadata extracted from two datasets, namely MovieLens and IMDB. As a result of our evaluation, we found out that the movies' genres and actors are the kind of description that generates better predictions for the considered content-based recommenders.