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Carnegie Mellon Robotics Institute

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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 respectto 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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Number of pages: 8

Text Reference

Charles Zitnick and Takeo Kanade, "Maximum Entropy for Collaborative Filtering," Proc. Twentieth Conference of Uncertainty in Artificial Intelligence, 2004, pp. 636 - 643.

BibTeX Reference

@inproceedings{Zitnick_2004_4872,
   author = "Charles Zitnick and Takeo Kanade",
   title = "Maximum Entropy for Collaborative Filtering",
   booktitle = "Proc. Twentieth Conference of Uncertainty in Artificial Intelligence",
   pages = "636 - 643",
   year = "2004",
}

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