Enhancing Semantics-Driven Recommender Systems with Visual Features

Content-based semantics-driven recommender systems are often used in the small-scale news recommendation domain, founded on the TF-IDF measure but also taking into account domain semantics through semantic lexicons or ontologies. This work explores the application of content-based semantics-driven recommender systems to large-scale recommendations on the example of movie domain. We propose methods to extract semantic features from various item descriptions, including images. In particular, we use computer vision to extract semantic features from images and use these for recommendation together with various features extracted from textual information. The semantics-driven approach is scaled up with pre-computation of the cosine similarities and gradient learning of the model. The results of the study on a large-scale MovieLens dataset of user ratings demonstrate that semantics-driven recommenders can be extended to more complex domains and outperform TF-IDF on ROC, PR, F-1, and Kappa metrics.