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» Learning to rank on graphs
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AAAI
2008
15 years 1 days ago
Semi-Supervised Ensemble Ranking
Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance b...
Steven C. H. Hoi, Rong Jin
ICDE
2009
IEEE
159views Database» more  ICDE 2009»
15 years 4 months ago
ApproxRank: Estimating Rank for a Subgraph
Abstract— Customized semantic query answering, personalized search, focused crawlers and localized search engines frequently focus on ranking the pages contained within a subgrap...
Yao Wu, Louiqa Raschid
SDM
2010
SIAM
153views Data Mining» more  SDM 2010»
14 years 11 months ago
Reconstruction from Randomized Graph via Low Rank Approximation
The privacy concerns associated with data analysis over social networks have spurred recent research on privacypreserving social network analysis, particularly on privacypreservin...
Leting Wu, Xiaowei Ying, Xintao Wu
TREC
2008
14 years 11 months ago
Weighted PageRank: Cluster-Related Weights
PageRank is a way to rank Web pages taking into account hyper-link structure of the Web. PageRank provides efficient and simple method to find out ranking of Web pages exploiting ...
Danil Nemirovsky, Konstantin Avrachenkov
NAACL
2010
14 years 7 months ago
Hitting the Right Paraphrases in Good Time
We present a random-walk-based approach to learning paraphrases from bilingual parallel corpora. The corpora are represented as a graph in which a node corresponds to a phrase, an...
Stanley Kok, Chris Brockett