| Abstract | Recent projects in collaborative filtering and information filtering address the task of inferring user prefer-ence relationships for products or information. The data on which these inferences are based typically con-
sists 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 recommen-
dations 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 repre-
sentation 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 to represent uncertainty about the existence of relationships in
the model and allow the properties of an object to depend probabilistically both on other properties of that
object and on properties of related objects.
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