Modeling User Exposure in Recommendation
Posted on23 Oct 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 Sep 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 Dec 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 Aug 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 Jun 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