Knowledge Enhanced Person-Job Fit for Talent Recruitment

As an essential task of talent recruitment, person-job fit aims to measure the matching degree between talent qualifi-cation and the job requirements of a position. Existing studies usually formulate this task as a long text matching problem with a focus on learning effective representations of both job postings and resumes. However, it is commonly known that there exists a semantic gap between textual job postings and textual resumes. Therefore, in this paper, we study how to improve person-job fit by bridging this semantic gap with the help of prior knowledge. To this end, we first design a distantly supervised skill extraction model to identify the skill entities from the given job postings and resumes using only unlabeled data and skill entity dictionaries. The identified skill entities will be used to construct a skill knowledge graph (KG) on the global corpus, which can provide the prior knowledge. Also, we propose a knowledge enhanced person-job fit approach for talent recruitment. Here, we model job postings and resumes as two graphs and fuse the prior external knowledge into the graph representation learning. Specifically, we first build the graphs from job posting and resume text. Then, we design a knowledge-aware graph encoder that can not only capture the contextual word relationships within each job posting or resume, but also incorporate the prior knowledge into node representation learning. In addition, we propose an interactive learning method to perform effective graph matching in both graph-level and node-level, respectively. Meanwhile, a multi-task learning strategy is introduced to facilitate the graph representation learning. Finally, extensive experiments conducted on real-world datasets have clearly validated the effectiveness of our approaches compared with state-of-the-art baselines.