基于随机游走模型的排序学习方法

Ranking Learning Method Based on Random Walk Model

【目的】通过引入随机游走模型,解决有监督排序学习中训练数据的标记信息难以获取的问题。【方法】提出一种基于重启随机游走模型的排序学习方法,通过游走模型完成训练数据的自动标注,降低排序学习对标记数据的依赖性,并在OHSUMED数据集上进行实验。【结果】当已标注样本在数据集中占比50%时,该方法能有效完成排序学习任务,与标注样本占比100%的排序学习算法相比,其排序效果明显优于Rank Net算法,略低于List Net算法。【局限】本文方法要求对每个查询单独进行随机游走,这对实际应用中多样查询下的文档标注工作来说仍然需要花费较多精力来完成。【结论】本文方法有很好的排序学习效果,能有效解决排序学习中训练数据的标注难题。

[Objective] This paper tries to obtain the tagging data of training corpus for supervised ranking learning tasks. [Methods] First, we proposed a ranking learning method based on the random walk model. Then, we used this method to automatically tag the training data, which also reduced the dependency of ranking on the tags. Finally, we examined our method with the OHSUMED data set. [Results] We finished the ranking learning tasks with only half of samples tagged. Compared with algorithms based on all tagged samples, performance of the proposed method was better than the Rank Net algorithm but not as good as the List Net one. [Limitations] Our method requires a random walk for each query, which is time consuming in practice. [Conclusions] The proposed method can effectively rank the learning results of training data.