Constructing a dynamic bayes net model of academic advising

In this paper we apply ideas from collaborative filtering to the problem of building dynamic Bayesian network (DBN) models for planning. We demonstrate that item-based collaborative filtering can be used to construct dynamic Bayesian networks for use in large, factored domains with sparse data. Such Bayesian networks can model the transition function for decision-theoretic planning. We demonstrate the feasibility and effectiveness of this technique on an academic advising domain, based on student grades in computer science and related courses at the University of Kentucky.