Plackett-Luce model for learning-to-rank task
Information Retrieval
2019-09-17 v1 Machine Learning
Abstract
List-wise based learning to rank methods are generally supposed to have better performance than point- and pair-wise based. However, in real-world applications, state-of-the-art systems are not from list-wise based camp. In this paper, we propose a new non-linear algorithm in the list-wise based framework called ListMLE, which uses the Plackett-Luce (PL) loss. Our experiments are conducted on the two largest publicly available real-world datasets, Yahoo challenge 2010 and Microsoft 30K. This is the first time in the single model level for a list-wise based system to match or overpass state-of-the-art systems in real-world datasets.
Cite
@article{arxiv.1909.06722,
title = {Plackett-Luce model for learning-to-rank task},
author = {Tian Xia and Shaodan Zhai and Shaojun Wang},
journal= {arXiv preprint arXiv:1909.06722},
year = {2019}
}