SVD Subspace Projections for Term Suggestion Ranking and Clustering
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چکیده
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 the effect of SVD and subspace projections on search results.
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تاریخ انتشار 2004