SVD Subspace Projections for Term Suggestion Ranking and Clustering
David F. Gleich, Harvey Mudd, Leonid E. Zhukov · 2004
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 e#ect of SVD and subspace projections on search results.