Higher-Order Web Link Analysis Using Multilinear Algebra
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Recommending questions using the mdl-based tree cut model
Proceedings of the 17th international conference on World Wide Web
Towards intent-driven bidterm suggestion
Proceedings of the 18th international conference on World wide web
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Query phrase suggestion from topically tagged session logs
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
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Proceedings of the 18th Brazilian symposium on Multimedia and the web
Fast dimension reduction for document classification based on imprecise spectrum analysis
Information Sciences: an International Journal
Preference-based mining of top-K influential nodes in social networks
Future Generation Computer Systems
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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.