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ICML
2005
IEEE
14 years 11 months ago
Learning to rank using gradient descent
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these...
Christopher J. C. Burges, Tal Shaked, Erin Renshaw...
WWW
2006
ACM
14 years 10 months ago
Automatic identification of user interest for personalized search
One hundred users, one hundred needs. As more and more topics are being discussed on the web and our vocabulary remains relatively stable, it is increasingly difficult to let the ...
Feng Qiu, Junghoo Cho
CIKM
2009
Springer
14 years 4 months ago
Generating synopses for document-element search
Scientists often search for document-elements like tables, figures, or algorithm pseudo-codes. Domain scientists and researchers report important data, results and algorithms usi...
Sumit Bhatia, Shibamouli Lahiri, Prasenjit Mitra
CIKM
2009
Springer
14 years 4 months ago
Feature selection for ranking using boosted trees
Modern search engines have to be fast to satisfy users, so there are hard back-end latency requirements. The set of features useful for search ranking functions, though, continues...
Feng Pan, Tim Converse, David Ahn, Franco Salvetti...
PREMI
2011
Springer
13 years 29 days ago
Finding Potential Seeds through Rank Aggregation of Web Searches
This paper presents a potential seed selection algorithm for web crawlers using a gain - share scoring approach. Initially we consider a set of arbitrarily chosen tourism queries. ...
Rajendra Prasath, Pinar Öztürk