  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.
