Uncertainty in Artificial Intelligence
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Dependency Networks for Collaborative Filtering and Data Visualization
David Heckerman, David Chickering, Christopher Meek, Robert Rounthwaite, Carl Kadie
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.
Keywords: Dependency networks, Bayesian networks, graphical
Pages: 264-273
PS Link:
PDF Link: /papers/00/p264-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman00,
AUTHOR = "David Heckerman and David Chickering and Christopher Meek and Robert Rounthwaite and Carl Kadie",
TITLE = "Dependency Networks for Collaborative Filtering and Data Visualization",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "264--273"
}


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