Neural Feature Selection for Learning to Rank
Abstract
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%.
Cite
@article{arxiv.2102.11345,
title = {Neural Feature Selection for Learning to Rank},
author = {Alberto Purpura and Karolina Buchner and Gianmaria Silvello and Gian Antonio Susto},
journal= {arXiv preprint arXiv:2102.11345},
year = {2021}
}