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Published Articles >> Table of Contents >> Abstract
Fourth IEEE International Conference on Data Mining (ICDM'04)
pp. 391-394
SVD based Term Suggestion and Ranking System
David Gleich, Harvey Mudd College, Claremont, CA
Leonid Zhukov, Yahoo! Research Labs, Pasadena, CA
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10006
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| Abstract |
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In this paper, we consider the application of the singular value decomposition (SVD) to a search term suggestion system in a pay-for-performance search market. We propose a novel positive and negative refinement method based on orthogonal subspace projections. We demonstrate that SVD subspace-based methods: 1) expand coverage by reordering the results, and 2) enhance the clustered structure of the data. The numerical experiments reported in this paper were performed on Overture's pay-per-performance search market data.
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 Additional Information
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Citation:
David Gleich, Leonid Zhukov,
"SVD based Term Suggestion and Ranking System,"
icdm,
pp. 391-394,
Fourth IEEE International Conference on Data Mining (ICDM'04),
2004
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