基于强化学习的多样性文档排序算法

在排序学习方法中,通过直接优化信息检索评价指标来学习排序模型的方法,取得了很好的排序效果,但是其损失函数在利用所有排序位置信息以及融合多样性排序因素方面还有待提高。为此,提出基于强化学习的多样性文档排序算法。首先,将强化学习思想应用于文档排序问题,通过将排序行为建模为马尔可夫决策过程,在每一次迭代过程中利用所有排序位置的信息,不断为每个排序位置选择最优的文档。其次,在排序过程中结合多样性策略,依据相似度阈值,裁剪高度相似的文档,从而保证排序结果的多样性。最后,在公共数据集上的实验结果表明,提出的算法在保证排序准确性的同时，增强了排序结果的多样性。

A diversity document ranking algorithmbased on reinforcement learning

In learning to rank methods, the method of learning the ranking model by directly optimizing the information retrieval evaluation indexes achieves good ranking effect, but its loss function still needs to be improved in using all ranking location information and fusing diversity ranking factors. Therefore, a diversity document ranking algorithm based on reinforcement learning is proposed. Firstly, the idea of reinforcement learning is applied to the ranking problem. By modeling the ranking behavior as a Markov decision process, the information of all ranking positions is used in each iteration to contin- uously select the optimal document for each ranking position. Secondly, the diversity strategy is used in the ranking process to cut highly similar documents according to the similarity threshold to ensure the diversity of the ranking results. Finally, the experimental results on the public dataset show that the proposed algorithm enhances the diversity of the ranking results while ensuring the ranking accuracy.