  With the ever-increasing growth of online recruitment data, job-resume
matching has become an important task to automatically match jobs with suitable
resumes. This task is typically casted as a supervised text matching problem.
Supervised learning is powerful when the labeled data is sufficient. However,
on online recruitment platforms, job-resume interaction data is sparse and
noisy, which affects the performance of job-resume match algorithms. To
alleviate these problems, in this paper, we propose a novel multi-view
co-teaching network from sparse interaction data for job-resume matching. Our
network consists of two major components, namely text-based matching model and
relation-based matching model. The two parts capture semantic compatibility in
two different views, and complement each other. In order to address the
challenges from sparse and noisy data, we design two specific strategies to
combine the two components. First, two components share the learned parameters
or representations, so that the original representations of each component can
be enhanced. More importantly, we adopt a co-teaching mechanism to reduce the
influence of noise in training data. The core idea is to let the two components
help each other by selecting more reliable training instances. The two
strategies focus on representation enhancement and data enhancement,
respectively. Compared with pure text-based matching models, the proposed
approach is able to learn better data representations from limited or even
sparse interaction data, which is more resistible to noise in training data.
Experiment results have demonstrated that our model is able to outperform
state-of-the-art methods for job-resume matching.
