Stochastic Top-k ListNet

ListNet is a well-known listwise learning to rank model and has gained much atten-tion in recent years. A particular problem of ListNet, however, is the high computa-tion complexity in model training, main-ly due to the large number of object per-mutations involved in computing the gra-dients. This paper proposes a stochastic ListNet approach which computes the gra-dient within a bounded permutation sub-set. It significantly reduces the computa-tion complexity of model training and al-lows extension to Top-k models, which is impossible with the conventional imple-mentation based on full-set permutation-s. Meanwhile, the new approach utilizes partial ranking information of human la-bels, which helps improve model quality. Our experiments demonstrated that the s-tochastic ListNet method indeed leads to better ranking performance and speeds up the model training remarkably.