Plackett-Luce model for learning-to-rank task

2019/09/15 by Tian Xia, Xia, Tian, Shaodan Zhai +3
Computer Science · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Optimization and Search Problems

paper · pdf · doi:10.48550/arxiv.1909.06722

openalex publication_date 2019/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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