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...
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 ...
Scientists often search for document-elements like tables, figures, or algorithm pseudo-codes. Domain scientists and researchers report important data, results and algorithms usi...
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...
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. ...