Personalised rating prediction for new users using latent factor models

Abstract

In recent years, personalised recommendations have gained importance in helping users deal with the abundance of information available online. Personalised recommendations are often based on rating predictions, and thus accurate rating prediction is essential for the generation of useful recommendations. Recently, rating prediction algorithms that are based on matrix factorisation have become increasingly popular, due to their high accuracy and scalability. However, these algorithms still deliver inaccurate rating predictions for new users, who submitted only a few ratings. In this paper, we address the new user problem by introducing several extensions to the basic matrix factorisation algorithm, which take user attributes into account when generating rating predictions. We consider both demographic attributes, explicitly supplied by users, and attributes inferred from user-generated texts. Our results show that employing our text-based user attributes yields personalised rating predictions that are more accurate than our baselines, while not requiring users to explicitly supply any information about themselves and their preferences.