Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the perfo...
We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit it...
We present a algorithm based on factor analysis for performing collaborative quality filtering (CQF). Unlike previous approaches to CQF, which estimate the consensus opinion of a...
While most popular collaborative filtering methods use low-rank matrix factorization and parametric density assumptions, this article proposes an approach based on distribution-fr...
Current recommender systems, based on collaborative filtering, implement a rather limited model of interaction. These systems intelligently elicit information from a user only dur...