模仿排序学习模型

文档排序一直是信息检索(IR)领域的关键任务之一。受益于马尔科夫决策过程强大的建模能力,以及强化学习方法强大的求解能力,近年来基于强化学习的排序模型被提出并取得了良好效果。然而,由于候选文档中会包含大量的不相关文档,导致基于"试错"的强化学习方法存在效率低下的问题。为解决上述问题,该文提出了一种基于模仿学习的排序学习算法IR-DAGGER,其基于文档标注信息构建专家策略,在保证文档排序精度的同时提高了算法的学习效率。为了测试IR-DAGGER的性能,该文基于面向相关性排序任务的OHSUMED数据集和面向多样化排序的TREC数据集进行了实验,实验结果表明IR-DAGGER在上述两个数据集上均提升了文档排序的精度和效率。

Imitation Learning to Rank

Document ranking is one of the central tasks in a number of IR applications. In recent years, efforts have been made to apply reinforcement learning for learning document ranking models and a number of methods have been developed. Though preliminary success has been achieved, existing reinforcement methods still suffer from the sparseness of the relevant documents. In this paper, we propose to involve ground-truth ranking lists during the learning process, achieving a novel imitation learning-based learning to rank algorithm called IR-DAGGER. It utilizes the ranking lists sampled by the expert policy, which can enhance the learning efficiency while keeping the ranking accuracies. Experimental results based on OHSUMED and TREC showed that IR-DAGGER can outperform the state-of-the-art baselines for the tasks of relevant ranking and diverse ranking, indicating the effectiveness and efficiency of imitation learning in document ranking.