Choice by Elimination via Deep Neural Networks
This addresses ranking problems in domains like search engines or recommendation systems, but appears incremental as it combines existing techniques (deep neural networks with probabilistic choice models).
The paper tackles the problem of learning to rank items by introducing Neural Choice by Elimination, which integrates deep neural networks into probabilistic sequential choice models. The method achieves competitive performance against state-of-the-art approaches on a large-scale dataset with over 425,000 items from the Yahoo! learning to rank challenge.
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.