Evaluating Ensemble Strategies for Recommender Systems under Metadata Reduction

Abstract

Recommender systems are information filtering tools that aspire to predict accurate ratings for users and items, with the ultimate goal of providing users with personalized and relevant recommendations. Recommender system that rely on the combination of quality metadata, i.e., all descriptive information about an item, are likely to be successful in the process of finding what is relevant or not for a target user. The problem arises when either data is sparse or important metadata is not available, making it hard for recommender systems to predict proper user-item ratings. In particular, this study investigates how our proposed collaborative-filtering recommender performs when important metadata is reduced from a dataset. To evaluate our approach use the HetRec 2011 2k dataset with five different movie metadata (genres, tags, directors, actors and countries). By applying our approach of metadata reduction, we provide a comprehensive analysis on how mean average precision is affected as important metadata become unavailable.