We introduce Neural Choice by Elimination, a new framework that integrates
deep neural networks into probabilistic sequential choice models for learning
to rank. Given a set of items to chose from, the elimination strategy starts
with the whole item set and iteratively eliminates the least worthy item in the
remaining subset. We prove that the choice by elimination is equivalent to
marginalizing out the random Gompertz latent utilities. Coupled with the choice
model is the recently introduced Neural Highway Networks for approximating
arbitrarily complex rank functions. We evaluate the proposed framework on a
large-scale public dataset with over 425K items, drawn from the Yahoo! learning
to rank challenge. It is demonstrated that the proposed method is competitive
against state-of-the-art learning to rank methods.

本文介绍了一种名为 Neural Choice by Elimination 的新框架，将深度神经网络集成到概率顺序选择模型中，用于学习排名。该框架集成了最近引入的 Neural Highway Networks 以近似任意复杂的排名函数，证明了本方法在大规模公共数据集上具有与最先进的学习排序方法相竞争的性能。