Abstract

Recent projects in collaborative filtering and information filtering address the task of inferring user preference relationships for products or information. The data on which these inferences are based typically consists of pairs of people and items. The items may be information sources (such as web pages or newspaper articles) or products (such as books, software, movies or CDs). We are interested in making recommendations or predictions. Traditional approaches to the problem derive from classical algorithms in statistical pattern recognition and machine learning. The majority of these approaches assume a "flat" data representation for each object, and focus on a single dyadic relationship between the objects. In this paper, we examine a richer model that allows us to reason about many different relations at the same time. We build on the recent work on probabilistic relational models (PRMs), and describe how PRMs can be applied to the task of collaborative filtering. PRMs allow us t...

Citations