Dependency Networks for Collaborative Filtering and Data Visualization

TL;DR

Dependency networks are a flexible graphical model allowing cyclic dependencies, useful for probabilistic inference, collaborative filtering, and data visualization, with efficient learning algorithms from data.

Contribution

This paper introduces dependency networks as an alternative to Bayesian networks, enabling cyclic dependencies and efficient learning for probabilistic tasks.

Findings

Dependency networks can model cyclic relationships.

Efficient algorithms for learning from data are proposed.

Application to collaborative filtering and visualization demonstrated.

Abstract

We describe a graphical model for probabilistic relationships---an alternative to the Bayesian network---called a dependency network. The graph of a dependency network, unlike a Bayesian network, is potentially cyclic. The probability component of a dependency network, like a Bayesian network, is a set of conditional distributions, one for each node given its parents. We identify several basic properties of this representation and describe a computationally efficient procedure for learning the graph and probability components from data. We describe the application of this representation to probabilistic inference, collaborative filtering (the task of predicting preferences), and the visualization of acausal predictive relationships.