Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity. (arXiv:2204.10872v2 [cs.LG] UPDATED)

Plackett-Luce gradient estimation enables the optimization of stochastic
ranking models within feasible time constraints through sampling techniques.
Unfortunately, the computational complexity of existing methods does not scale
well with the length of the rankings, i.e. the ranking cutoff, nor with the
item collection size. In this paper, we introduce the novel PL-Rank-3 algorithm
that performs unbiased gradient estimation with a computational complexity
comparable to the best sorting algorithms. As a result, our novel
learning-to-rank method is applicable in any scenario …

arxiv complexity computational gradient learning learning-to-rank sampling