Uncertainty in Artificial Intelligence
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Maximum Entropy for Collaborative Filtering
Lawrence Zitnick, Takeo Kanade
Abstract:
Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higher-order interactions is generally not present. Second, the variables that we are conditioning upon vary for each query. That is, users label different variables during each query. For this reason, there is no consistent input to output mapping. To address these problems we purpose a maximum entropy approach using a non-standard measure of entropy. This approach can be simplified to solving a set of linear equations that can be efficiently solved.
Keywords: null
Pages: 636-643
PS Link:
PDF Link: /papers/04/p636-zitnick.pdf
BibTex:
@INPROCEEDINGS{Zitnick04,
AUTHOR = "Lawrence Zitnick and Takeo Kanade",
TITLE = "Maximum Entropy for Collaborative Filtering",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "636--643"
}


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