  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.
