Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit...Read More

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items...Read More

We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences. CTPF can be used to build recommender...Read More

We introduce a novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents. CSTM’s chief novelty lies...Read More

Effective learning of user preferences is critical to easing user burden in various types of matching problems. Equally important is active query...Read More