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Person-Job Fit is the process of matching the right talent for the right jobby identifying talent competencies that are required for the job. While manyqualitative efforts have been made in related fields, it still lacks ofquantitative ways of measuring talent competencies as well as the job's talentrequirements. To this end, in this paper, we propose a novel end-to-enddata-driven model based on Convolutional Neural Network (CNN), namelyPerson-Job Fit Neural Network (PJFNN), for matching a talent qualification tothe requirements of a job. To be specific, PJFNN is a bipartite neural networkwhich can effectively learn the joint representation of Person-Job fitness fromhistorical job applications. In particular, due to the design of a hierarchicalrepresentation structure, PJFNN can not only estimate whether a candidate fitsa job, but also identify which specific requirement items in the job postingare satisfied by the candidate by measuring the distances between correspondinglatent representations. Finally, the extensive experiments on a large-scalereal-world dataset clearly validate the performance of PJFNN in terms ofPerson-Job Fit prediction. Also, we provide effective data visualization toshow some job and talent benchmark insights obtained by PJFNN.

Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning