Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach

TL;DR

This paper introduces an ability-aware neural network model for improving person-job fit in recruitment by leveraging historical application data and attention mechanisms to enhance interpretability and matching accuracy.

Contribution

The paper presents a novel end-to-end neural network that uses hierarchical attention and semantic representations to better match candidates to jobs, reducing manual effort.

Findings

Outperforms baseline models in matching accuracy

Provides interpretable insights into candidate-job fit

Effectively utilizes large-scale real-world data

Abstract

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.