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.

中文翻译：

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推荐MOOC中的作业课程：自动弱监督方法

大规模的在线公开课程（MOOC）的激增要求针对招聘网站上发布的职位，特别是那些采用MOOC的人寻找新工作的人，提供有效的课程推荐方法。尽管有监督的排名模型取得了进步，但缺乏足够的有监督的信号使我们无法直接学习有监督的排名模型。本文通过强化学习提出了一个通用的自动化弱监督框架AutoWeakS，以解决该问题。一方面，该框架使得能够在由多个无监督等级模型产生的伪标签上训练多个有监督等级模型。另一方面，该框架可以自动搜索这些监督模型和非监督模型的最佳组合。系统地 我们在来自不同招聘网站和MOOCs平台的课程的多个工作数据集上评估了该模型。实验表明，我们的模型明显优于经典的无监督，有监督和弱监督基线。