一致性(知识库)
计算机科学
排名(信息检索)
稳健性
秩(图论)
学习排名
功能(生物学)
信息丢失
算法
机器学习
人工智能
数学
组合数学
进化生物学
生物
程序设计语言
作者
Fang Xia,Tie-Yan Liu,Jue Wang,Wensheng Zhang,Hang Li
标识
DOI:10.1145/1390156.1390306
摘要
This paper aims to conduct a study on the listwise approach to learning to rank. The listwise approach learns a ranking function by taking individual lists as instances and minimizing a loss function defined on the predicted list and the ground-truth list. Existing work on the approach mainly focused on the development of new algorithms; methods such as RankCosine and ListNet have been proposed and good performances by them have been observed. Unfortunately, the underlying theory was not sufficiently studied so far. To amend the problem, this paper proposes conducting theoretical analysis of learning to rank algorithms through investigations on the properties of the loss functions, including consistency, soundness, continuity, differentiability, convexity, and efficiency. A sufficient condition on consistency for ranking is given, which seems to be the first such result obtained in related research. The paper then conducts analysis on three loss functions: likelihood loss, cosine loss, and cross entropy loss. The latter two were used in RankCosine and ListNet. The use of the likelihood loss leads to the development of a new listwise method called ListMLE, whose loss function offers better properties, and also leads to better experimental results.
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