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» Learning to rank for information retrieval (LR4IR 2008)
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ECIR
2009
Springer
16 years 3 months ago
Regression Rank: Learning to Meet the Opportunity of Descriptive Queries
Abstract. We present a new learning to rank framework for estimating context-sensitive term weights without use of feedback. Specifically, knowledge of effective term weights on ...
Matthew Lease, James Allan, W. Bruce Croft
IR
2010
15 years 4 months ago
Adapting boosting for information retrieval measures
Abstract We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empiricall...
Qiang Wu, Christopher J. C. Burges, Krysta Marie S...
CIKM
2010
Springer
15 years 4 months ago
Learning to rank relevant and novel documents through user feedback
We consider the problem of learning to rank relevant and novel documents so as to directly maximize a performance metric called Expected Global Utility (EGU), which has several de...
Abhimanyu Lad, Yiming Yang
137
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CIKM
2009
Springer
16 years 20 days ago
On domain similarity and effectiveness of adapting-to-rank
Adapting to rank address the the problem of insufficient domainspecific labeled training data in learning to rank. However, the initial study shows that adaptation is not always...
Keke Chen, Jing Bai, Srihari Reddy, Belle L. Tseng
CIKM
2009
Springer
16 years 20 days ago
Learning to rank graphs for online similar graph search
Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chem...
Bingjun Sun, Prasenjit Mitra, C. Lee Giles