Learning to rank

Known as: Machine learned ranking, Machine learned relevance, MLR 
Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement… (More)
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Papers overview

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Highly Cited
2015
Highly Cited
2015
Learning a similarity function between pairs of objects is at the core of learning to rank approaches. In information retrieval… (More)
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Highly Cited
2013
Highly Cited
2013
Many computer vision problems have an asymmetric distribution of information between training and test time. In this work, we… (More)
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Highly Cited
2010
Highly Cited
2010
We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the… (More)
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Highly Cited
2009
Highly Cited
2009
Pairwise learning to rank methods such as RankSVM give good performance, but suffer from the computational burden of optimizing… (More)
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Highly Cited
2008
Highly Cited
2008
This paper aims to conduct a study on the listwise approach to learning to rank. The listwise approach learns a ranking function… (More)
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Highly Cited
2007
Highly Cited
2007
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank… (More)
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Highly Cited
2007
Highly Cited
2007
This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central problem for information… (More)
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Highly Cited
2006
Highly Cited
2006
The quality measures used in information retrieval are particularly difficult to optimize directly, since they depend on the… (More)
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Highly Cited
2005
Highly Cited
2005
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function… (More)
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Highly Cited
2005
Highly Cited
2005
New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training… (More)
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