We introduce a novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents. CSTM’s chief novelty lies in its learned model of individual libraries, or sets of documents, associated with each user. Overall, CSTM is a joint directed probabilistic model of user-item scores (ratings), and the textual side information in the user libraries and the items. Creating a generative description of scores and the text allows CSTM to perform well in a wide variety of data regimes, smoothly combining the side information with observed ratings as the number of ratings available for a given user ranges from none to many. Experiments on real-world datasets demonstrate CSTM’s performance. We further demonstrate its utility in an application for personal recommendations of posters which we deployed at the NIPS 2013 conference.
Laurent Charlin, Richard S. Zemel, and Hugo Larochelle, Leveraging user libraries to bootstrap collaborative filtering, in: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), ACM, New York, NY, USA, 2014