Papers › Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical...
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, Stefan Kramer
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.
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