SVD Subspace Projections for Term Suggestion Ranking (2004)
by
And Clustering David
,
David Gleich
In Technical Report, Yahoo! Research Labs
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@INPROCEEDINGS{David04svdsubspace,
author = {And Clustering David and David Gleich},
title = {SVD Subspace Projections for Term Suggestion Ranking},
booktitle = {In Technical Report, Yahoo! Research Labs},
year = {2004}
}
author = {And Clustering David and David Gleich},
title = {SVD Subspace Projections for Term Suggestion Ranking},
booktitle = {In Technical Report, Yahoo! Research Labs},
year = {2004}
}
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Abstract:
In this manuscript, we evaluate 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 relevance feedback method for search refinement based on orthogonal subspace projections. We apply these methods to the subset of Overture's market data and demonstrate a clustering effect of SVD.
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