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» Learning to rank for information retrieval (LR4IR 2008)
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SIGIR
2012
ACM
13 years 8 months ago
Parallelizing ListNet training using spark
As ever-larger training sets for learning to rank are created, scalability of learning has become increasingly important to achieving continuing improvements in ranking accuracy [...
Shilpa Shukla, Matthew Lease, Ambuj Tewari
WWW
2010
ACM
15 years 6 months ago
Classification-enhanced ranking
Many have speculated that classifying web pages can improve a search engine's ranking of results. Intuitively results should be more relevant when they match the class of a q...
Paul N. Bennett, Krysta Marie Svore, Susan T. Duma...
WWW
2011
ACM
15 years 18 days ago
Identifying primary content from web pages and its application to web search ranking
Web pages are usually highly structured documents. In some documents, content with different functionality is laid out in blocks, some merely supporting the main discourse. In ot...
Srinivas Vadrevu, Emre Velipasaoglu
JCST
2008
138views more  JCST 2008»
15 years 6 months ago
Predicting Chinese Abbreviations from Definitions: An Empirical Learning Approach Using Support Vector Regression
In Chinese, phrases and named entities play a central role in information retrieval. Abbreviations, however, make keyword-based approaches less effective. This paper presents an em...
Xu Sun, Houfeng Wang, Bo Wang 0003
WWW
2007
ACM
16 years 6 months ago
Web projections: learning from contextual subgraphs of the web
Graphical relationships among web pages have been leveraged as sources of information in methods for ranking search results. To date, specific graphical properties have been used ...
Jure Leskovec, Susan T. Dumais, Eric Horvitz