DMCA
1 Collaborative Filtering with Maximum Entropy
BibTeX
@MISC{Pavlov_1collaborative,
author = {Dmitry Pavlov and Eren Manavoglu and David M. Pennock and C. Lee Giles},
title = {1 Collaborative Filtering with Maximum Entropy},
year = {}
}
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Abstract
Abstract — We describe a novel maximum entropy (maxent) approach for generating online recommendations as a user navigates through a collection of documents. We show how to handle high-dimensional sparse data and represent it as a collection of ordered sequences of document requests. Our representation and the maxent approach have several advantages: (1) we can naturally model long-term interactions and dependencies in the data sequences; (2) we can query the model quickly once it is learned, which makes the method applicable to highvolume Web servers; and (3) we obtain empirically high quality recommendations. Although maxent learning is computationally infeasible if implemented in the straightforward way, we explore data clustering and several algorithmic techniques to make learning practical even in high dimensions. We present several methods for combining the predictions of maxent models learned
Keyphrases
maximum entropy maxent approach present several method online recommendation high-dimensional sparse data novel maximum entropy document request data sequence high quality recommendation web server maxent model data clustering straightforward way high dimension long-term interaction several advantage several algorithmic technique maxent learning ordered sequence
