Pairwise Learning-to-Rank Algorithm

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A Pairwise Learning-to-Rank Algorithm is a supervised ranking algorithm that compares item pairs.



References

2017a

2017b

Year Name Type Notes
2000 Ranking SVM (RankSVM) [1] pairwise A more recent exposition is in, which describes an application to ranking using clickthrough logs.
2006 IR-SVM [2] pairwise Ranking SVM with query-level normalization in the loss function.
2006 LambdaRank [3] pairwise/listwise RankNet in which pairwise loss function is multiplied by the change in the IR metric caused by a swap.
2007 FRank pairwise Based on RankNet, uses a different loss function - fidelity loss.
2007 GBRank pairwise
2007 QBRank pairwise
2007 RankRLS pairwise

Regularized least-squares based ranking. The work is extended in

to learning to rank from general preference graphs.

2008 LambdaMART pairwise/listwise Winning entry in the recent Yahoo Learning to Rank competition used an ensemble of LambdaMART models. C. Burges. (2010). From RankNet to LambdaRank to LambdaMART: An Overview.
2008 Ranking Refinement Rong Jin, Hamed Valizadegan, Hang Li, Ranking Refinement and Its Application for Information Retrieval, in International Conference on World Wide Web (WWW), 2008. pairwise A semi-supervised approach to learning to rank that uses Boosting.
2008 SSRankBoost Massih-Reza Amini, Vinh Truong, Cyril Goutte, A Boosting Algorithm for Learning Bipartite Ranking Functions with Partially Labeled Data, International ACM SIGIR conference, 2008. The code is available for research purposes. pairwise An extension of RankBoost to learn with partially labeled data (semi-supervised learning to rank)
2008 SortNet Leonardo Rigutini, Tiziano Papini, Marco Maggini, Franco Scarselli, "SortNet: learning to rank by a neural-based sorting algorithm", SIGIR 2008 workshop: Learning to Rank for Information Retrieval, 2008 pairwise SortNet, an adaptive ranking algorithm which orders objects using a neural network as a comparator.
2009 MPBoost pairwise Magnitude-preserving variant of RankBoost. The idea is that the more unequal are labels of a pair of documents, the harder should the algorithm try to rank them.
2010 GBlend pairwise Extends GBRank to the learning-to-blend problem of jointly solving multiple learning-to-rank problems with some shared features.
2010 IntervalRank pairwise & listwise
2010 CRR pointwise & pairwise Combined Regression and Ranking. Uses stochastic gradient descent to optimize a linear combination of a pointwise quadratic loss and a pairwise hinge loss from Ranking SVM.

2016

2007