Applying Multi-View Based Metadata in Personalized Ranking for Recommender Systems

In this paper, we propose a multi-view based metadata extraction technique from unstructured textual content in order to be applied in recommendation algorithms based on latent factors. The solution aims at reducing the problem of intense and time-consuming human effort to identify, collect and label descriptions about the items. Our proposal uses a unsupervised learning method to construct topic hierarchies with named entity recognition as privileged information. We evaluate the technique using different recommendation algorithms, and show that better accuracy is obtained when additional information about items is considered.