6533b851fe1ef96bd12a9755

RESEARCH PRODUCT

Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance

Alexander SegnerStefan KramerMarius KöppelMartin WagenerLukas PenselAndreas Karwath

subject

Transitive relationPairwise learningTheoretical computer scienceArtificial neural networkAntisymmetric relationComputer scienceRank (computer programming)Structure (category theory)Pairwise comparisonLearning to rank

description

We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.

yearjournalcountryeditionlanguage
2020-01-01
https://doi.org/10.1007/978-3-030-46133-1_15