An Explainable Person-Job Fit Model Incorporating Structured Information

Yunchong Zhang, Baisong Liu, Jiangbo Qian, Jiangcheng Qin, Xueyuan Zhang, Xueyong Jiang

Abstract: As the number of online job postings and users grows dramatically, the accuracy and explainability of personjob fit systems are of increasing concern. An explainable personjob fit system can show reasons when making recommendations to both Human Resources and Job Seekers, building trust between uses and recommendation system while providing accurate recommendation results. However, the existing research on content-based person-job fit mainly focuses on 1) dealing with unstructured statements without effectively using structured information in resumes and jobs, and 2) the explanations of the model stay at the level of giving a few sentences, which leads to a lack of explanations. In this paper, we propose an explainable person-job fit model based on the attention mechanism. We model the resume text through a hierarchical attention mechanism and capture the semantic connections between the resume, structured job text, and unstructured job text through a collaborative attention mechanism to better model the job content and provide both structured and unstructured levels of recommendation explanation. Experiments on a large real dataset show that our model outperforms existing baseline models and provides job recommendation reasons at both levels.