  Person-job fit is to match candidates and job posts on online recruitment
platforms using machine learning algorithms. The effectiveness of matching
algorithms heavily depends on the learned representations for the candidates
and job posts. In this paper, we propose to learn comprehensive and effective
representations of the candidates and job posts via feature fusion. First, in
addition to applying deep learning models for processing the free text in
resumes and job posts, which is adopted by existing methods, we extract
semantic entities from the whole resume (and job post) and then learn features
for them. By fusing the features from the free text and the entities, we get a
comprehensive representation for the information explicitly stated in the
resume and job post. Second, however, some information of a candidate or a job
may not be explicitly captured in the resume or job post. Nonetheless, the
historical applications including accepted and rejected cases can reveal some
implicit intentions of the candidates or recruiters. Therefore, we propose to
learn the representations of implicit intentions by processing the historical
applications using LSTM. Last, by fusing the representations for the explicit
and implicit intentions, we get a more comprehensive and effective
representation for person-job fit. Experiments over 10 months real data show
that our solution outperforms existing methods with a large margin. Ablation
studies confirm the contribution of each component of the fused representation.
The extracted semantic entities help interpret the matching results during the
case study.
