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» Learning to rank for information retrieval (LR4IR 2008)
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CIKM
2008
Springer
15 years 1 months ago
Are click-through data adequate for learning web search rankings?
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Zhicheng Dou, Ruihua Song, Xiaojie Yuan, Ji-Rong W...
SIGIR
2012
ACM
13 years 2 months ago
Robust ranking models via risk-sensitive optimization
Many techniques for improving search result quality have been proposed. Typically, these techniques increase average effectiveness by devising advanced ranking features and/or by...
Lidan Wang, Paul N. Bennett, Kevyn Collins-Thompso...
SIGIR
2008
ACM
14 years 11 months ago
Combining document- and paragraph-based entity ranking
We study entity ranking on the INEX entity track and propose a simple graph-based ranking approach that enables to combine scores on document and paragraph level. The combined app...
Henning Rode, Pavel Serdyukov, Djoerd Hiemstra
CIKM
2008
Springer
15 years 1 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...
WWW
2011
ACM
14 years 6 months ago
Ranking in context-aware recommender systems
As context is acknowledged as an important factor that can affect users’ preferences, many researchers have worked on improving the quality of recommender systems by utilizing ...
Minsuk Kahng, Sangkeun Lee, Sang-goo Lee