Stochastic Top-k ListNet
This work addresses efficiency issues in learning-to-rank models for information retrieval, though it is incremental as it builds on the existing ListNet framework.
The paper tackles the high computational complexity of ListNet training by proposing a stochastic approach that computes gradients using a bounded permutation subset, which significantly reduces training time and enables extension to Top-k models, while also improving ranking performance.
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.