Pairwise learning to rank by neural networks revisited: reconstruction, theoretical analysis and practical performance
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Date
2025-04
Publication Type
Journal Article
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Abstract
We reevaluate the pairwise learning to rank approach based on neural nets, called RankNet, and present a theoretical analysis of its architecture. We show mathematically that the model can, under certain conditions, learn reflexive, antisymmetric, and transitive relations, enabling simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that the model outperforms numerous state-of-the-art methods (including a listwise approach), while being inherently simpler in structure and using a pairwise approach only.
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published
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Volume
114 (4)
Pages / Article No.
112
Publisher
Springer
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Subject
Information retrieval; Machine learning; Learning to rank