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» Magnitude-preserving ranking algorithms
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CIKM
2008
Springer
15 years 8 months ago
Suppressing outliers in pairwise preference ranking
Many of the recently proposed algorithms for learning feature-based ranking functions are based on the pairwise preference framework, in which instead of taking documents in isola...
Vitor R. Carvalho, Jonathan L. Elsas, William W. C...
ML
2010
ACM
185views Machine Learning» more  ML 2010»
15 years 25 days ago
Learning to rank on graphs
Graph representations of data are increasingly common. Such representations arise in a variety of applications, including computational biology, social network analysis, web applic...
Shivani Agarwal
SDM
2011
SIAM
414views Data Mining» more  SDM 2011»
14 years 8 months ago
Clustered low rank approximation of graphs in information science applications
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
Berkant Savas, Inderjit S. Dhillon
SEMWEB
2005
Springer
15 years 11 months ago
Semantically Rich Recommendations in Social Networks for Sharing, Exchanging and Ranking Semantic Context
Recommender algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, a...
Stefania Ghita, Wolfgang Nejdl, Raluca Paiu
SDM
2007
SIAM
107views Data Mining» more  SDM 2007»
15 years 7 months ago
On Demand Phenotype Ranking through Subspace Clustering
High throughput biotechnologies have enabled scientists to collect a large number of genetic and phenotypic attributes for a large collection of samples. Computational methods are...
Xiang Zhang, Wei Wang 0010, Jun Huan