The wide spread use of online recruitment services has led to information
explosion in the job market. As a result, the recruiters have to seek the
intelligent ways for Person Job Fit, which is the bridge for adapting the right
job seekers to the right positions. Existing studies on Person Job Fit have a
focus on measuring the matching degree between the talent qualification and the
job requirements mainly based on the manual inspection of human resource
experts despite of the subjective, incomplete, and inefficient nature of the
human judgement. To this end, in this paper, we propose a novel end to end
Ability aware Person Job Fit Neural Network model, which has a goal of reducing
the dependence on manual labour and can provide better interpretation about the
fitting results. The key idea is to exploit the rich information available at
abundant historical job application data. Specifically, we propose a word level
semantic representation for both job requirements and job seekers’ experiences
based on Recurrent Neural Network. Along this line, four hierarchical ability
aware attention strategies are designed to measure the different importance of
job requirements for semantic representation, as well as measuring the
different contribution of each job experience to a specific ability
requirement. Finally, extensive experiments on a large scale real world data
set clearly validate the effectiveness and interpretability of the APJFNN
framework compared with several baselines.

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