Dependency Networks for Collaborative Filtering and Data Visualization
This work provides a new probabilistic framework for collaborative filtering and data analysis, though it appears incremental as an alternative to existing models like Bayesian networks.
The authors introduced dependency networks, a graphical model alternative to Bayesian networks that allows cyclic graphs, and applied it to collaborative filtering and data visualization, demonstrating its utility in predicting preferences and visualizing acausal relationships.
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.