Addressing Scalability Issues in Semantics-Driven Recommender Systems

Abstract

Content-based semantics-driven recommender systems are often used in the small-scale news recommendation domain. These recommender systems improve over TF-IDF by taking into account (domain) semantics through semantic lexicons or domain ontologies. Our work explores the application of such recommender systems to other domains, using the case of large-scale movie recommendations. We propose new methods to extract semantic features from various item descriptions, and for scaling up the semantics-driven approach with pre-computation of the cosine similarities and gradient learning of the model. The results of the study on a large-scale dataset of user ratings demonstrate that semantics-driven recommenders can be extended to more complex domains and outperform TF-IDF on ROC, PR, F1, and Kappa metrics.