  LEarning TO Rank (LETOR) is a research area in the field of Information
Retrieval (IR) where machine learning models are employed to rank a set of
items. In the past few years, neural LETOR approaches have become a competitive
alternative to traditional ones like LambdaMART. However, neural architectures
performance grew proportionally to their complexity and size. This can be an
obstacle for their adoption in large-scale search systems where a model size
impacts latency and update time. For this reason, we propose an
architecture-agnostic approach based on a neural LETOR model to reduce the size
of its input by up to 60% without affecting the system performance. This
approach also allows to reduce a LETOR model complexity and, therefore, its
training and inference time up to 50%.
