Modeling User Exposure in Recommendation
Posted on23/10/2015
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
Dynamic Poisson Factorization
Posted on23/09/2015
Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items... Read More
Content-based recommendations with Poisson factorization
Posted on23/12/2014
We develop collaborative topic Poisson factorization (CTPF), a generative model of articles and reader preferences. CTPF can be used to build recommender... Read More
Leveraging User Libraries to Bootstrap Collaborative Filtering
Posted on23/08/2014
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
Active Learning for Matching Problems
Posted on23/06/2012
Effective learning of user preferences is critical to easing user burden in various types of matching problems. Equally important is active query... Read More