Towards Explaining Latent Factors with Topic Models in Collaborative Recommender Systems

Latent factor models have been proved to be the state of the art for the Collaborative Filtering approach in a Recommender System. However, latent factors obtained with mathematical methods applied to the user-item matrix can be hardly interpreted by humans. In this paper we exploit Topic Models applied to textual data associated with items to find explanations for latent factors. Based on the Movie Lens dataset and textual data about movies collected from Freebase we run a user study with over hundred participants to develop a reference dataset for evaluating different strategies towards more interpretable and portable latent factor models.