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» Learning effective ranking functions for newsgroup search
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SIGIR
2008
ACM
13 years 7 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff
ACG
2003
Springer
14 years 26 days ago
Evaluation Function Tuning via Ordinal Correlation
Heuristic search effectiveness depends directly upon the quality of heuristic evaluations of states in the search space. We show why ordinal correlation is relevant to heuristic se...
Dave Gomboc, T. Anthony Marsland, Michael Buro
CIKM
2006
Springer
13 years 11 months ago
Optimisation methods for ranking functions with multiple parameters
Optimising the parameters of ranking functions with respect to standard IR rank-dependent cost functions has eluded satisfactory analytical treatment. We build on recent advances ...
Michael J. Taylor, Hugo Zaragoza, Nick Craswell, S...
WEBI
2010
Springer
13 years 5 months ago
How to Improve Your Google Ranking: Myths and Reality
Abstract--Search engines have greatly influenced the way people access information on the Internet as such engines provide the preferred entry point to billions of pages on the Web...
Ao-Jan Su, Y. Charlie Hu, Aleksandar Kuzmanovic, C...
IPM
2008
100views more  IPM 2008»
13 years 7 months ago
Query-level loss functions for information retrieval
Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since...
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng W...