Pairwise Learning-to-Rank Algorithm

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

• Context:
  • It can be implemented by a Pairwise LTR system (to solve a pairwise LTR task).
  • It can range from being a Factorization-based Pairwise LTR Algorithm to being an SVM-based Pairwise LTR Algorithm to being ...
  • …
• Example(s):
  • BPR Algorithm.
  • RankingSVM.
  • …
• Counter-Example(s):
  • Pointwise LTR Algorithm.
  • Listwise LTR Algorithm.
• See: Learning-to-Rank System, Binary Classifier.

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References

2017a

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/learning_to_rank#Pairwise_approach Retrieved:2017-9-13.
  • In this case learning-to-rank problem is approximated by a classification problem — learning a binary classifier that can tell which document is better in a given pair of documents. The goal is to minimize average number of inversions in ranking.

2017b

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Learning_to_rank#List_of_methods Retrieved:2017-9-13.
  • A partial list of published learning-to-rank algorithms is shown below with years of first publication of each method
    (Note: as most supervised learning algorithms can be applied to pointwise case, only those methods which are specifically designed with ranking in mind are shown):

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

• (Guo et al., 2016) ⇒ Weiyu Guo, Shu Wu, Liang Wang, and Tieniu Tan. (2016). “Personalized Ranking with Pairwise Factorization Machines.” In: Neurocomputing Journal, 214(C). doi:10.1016/j.neucom.2016.05.074
  • QUOTE: Pairwise learning is a vital technique for personalized ranking with implicit feedback.

2007

• (Cao et al., 2007) ⇒ Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. (2007). “Learning to Rank: From Pairwise Approach to Listwise Approach.” In: Proceedings of the 24th International Conference on Machine learning. ISBN:978-1-59593-793-3 doi:10.1145/1273496.1273513
  • QUOTE: The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative filtering, and many other applications. Several methods for learning to rank have been proposed, which take object pairs as 'instances' in learning. We refer to them as the pairwise approach in this paper. ...

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