  ListNet is a well-known listwise learning to rank model and has gained much
attention in recent years. A particular problem of ListNet, however, is the
high computation complexity in model training, mainly due to the large number
of object permutations involved in computing the gradients. This paper proposes
a stochastic ListNet approach which computes the gradient within a bounded
permutation subset. It significantly reduces the computation complexity of
model training and allows extension to Top-k models, which is impossible with
the conventional implementation based on full-set permutations. Meanwhile, the
new approach utilizes partial ranking information of human labels, which helps
improve model quality. Our experiments demonstrated that the stochastic ListNet
method indeed leads to better ranking performance and speeds up the model
training remarkably.
