论文标题
推荐MOOC的课程求职：一种自动弱监督方法
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
论文作者
论文摘要
大规模开放的在线课程（MOOC）的扩散需要一种有效的方法，当然，对于招聘网站上发布的工作，特别是对于那些带MOOC寻找新工作的人来说。尽管有监督的排名模型的进步，但缺乏足够的监督信号使我们无法直接学习监督的排名模型。本文提出了一个通用自动化的弱监督框架，通过加强学习来解决问题。一方面，该框架可以在由多个无监督的排名模型产生的伪标签上培训多个监督排名模型。另一方面，该框架可以自动搜索这些受监督和无监督模型的最佳组合。从系统的角度来看，我们在来自MOOCS平台的不同招聘网站和课程的几个工作数据集上评估了所提出的模型。实验表明，我们的模型大大优于经典的无监督，监督和弱的监督基线。
The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who take MOOCs to find new jobs. Despite the advances of supervised ranking models, the lack of enough supervised signals prevents us from directly learning a supervised ranking model. This paper proposes a general automated weak supervision framework AutoWeakS via reinforcement learning to solve the problem. On the one hand, the framework enables training multiple supervised ranking models upon the pseudo labels produced by multiple unsupervised ranking models. On the other hand, the framework enables automatically searching the optimal combination of these supervised and unsupervised models. Systematically, we evaluate the proposed model on several datasets of jobs from different recruitment websites and courses from a MOOCs platform. Experiments show that our model significantly outperforms the classical unsupervised, supervised and weak supervision baselines.
