Listwise Learning-to-Rank Algorithm

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A Listwise Learning-to-Rank Algorithm is a learning-to-rank algorithm that can be implemented by a listwise LTR system (to solve a listwise LTR task).



References

2017

  • (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/learning_to_rank#Listwise_approach Retrieved:2017-9-13.
    • These algorithms try to directly optimize the value of one of the above evaluation measures, averaged over all queries in the training data. This is difficult because most evaluation measures are not continuous functions with respect to ranking model's parameters, and so continuous approximations or bounds on evaluation measures have to be used.

2017b

Year Name Type Notes
2006 LambdaRank pairwise/listwise RankNet in which pairwise loss function is multiplied by the change in the IR metric caused by a swap.
2007 AdaRank listwise
2007 ListNet listwise
2007 RankCosine listwise
2007 RankGP listwise
2007 SVMmap listwise
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 SoftRank listwise
2009 BoltzRank listwise Unlike earlier methods, BoltzRank produces a ranking model that looks during query time not just at a single document, but also at pairs of documents.
2009 BayesRank listwise A method combines Plackett-Luce Model and neural network to minimize the expected Bayes risk, related to NDCG, from the decision-making aspect.
2010 NDCG Boost Hamed Valizadegan, Rong Jin, Ruofei Zhang, Jianchang Mao, Learning to Rank by Optimizing NDCG Measure, in Proceeding of Neural Information Processing Systems (NIPS), 2010. listwise A boosting approach to optimize NDCG.
2010 IntervalRank pairwise & listwise
2017 ES-Rank listwise Evolutionary Strategy Learning to Rank technique with 7 fitness evaluation metrics

2007