Interview Choice Reveals Your Preference on the Market

Abstract

Online recruitment services are now rapidly changing the landscape of hiring traditions on the job market. There are millions of registered users with resumes, and tens of thousands of job postings available on the Web. Learning good job-resume matching for recruitment services is important. Existing studies on job-resume matching generally focus on learning good representations of job descriptions and resume texts with comprehensive matching structures. We assume that it would bring benefits to learn the preference of both recruiters and job-seekers from previous interview histories and expect such preference is helpful to improve job-resume matching. To this end, in this paper, we propose a novel matching network with preference modeled. The key idea is to explore the latent preference given the history of all interviewed candidates for a job posting and the history of all job applications for a particular talent. To be more specific, we propose a profiling memory module to learn the latent preference representation by interacting with both the job and resume sides. We then incorporate the preference into the matching framework as an end-to-end learnable neural network. Based on the real-world data from an online recruitment platform namely "Boss Zhipin", the experimental results show that the proposed model could improve the jobresume matching performance against a series of state-ofthe-art methods. In this way, we demonstrate that recruiters and talents indeed have preference and such preference can improve job-resume matching on the job market. * indicates equal contribution.

Learning Effective Representations for Person-Job Fit by Feature Fusion

“…These models can be adopted in our algorithm for learning representations of free text in resumes and job posts. The most relevant works are [23,24,32,34]. They all apply deep neural networks to learn representations for the resumes and jobs.…”

“…The multiple ability-aware representations of the job post and the resume are combined and then fed into a binary classification sub-network. Yan et al [32] analyze the interview history of the candidate and the job post to infer their preference. Memory networks are adopted to embed the preference in the representations of the candidate and the job post.…”

“…• APJFNN [23] applies attention modelling recursively to learn ability-aware representations for the resume and job post. • JRMPM [32] also exploits the application history. It uses a memory network to embed the preference of the candidate and the job recruiter into their representations.…”

“…The codes of the PJFNN, APJFNN and JRMPM are kindly shared by the authors of [4,32]. Evaluation Metrics Following previous works [23,32], we evaluate the accuracy, AUC, precision, recall and F1 of each method. We tune the threshold for generating the decision (accept or reject) over the validation dataset to maximize the F1 metric.…”

“…Recently, deep learning models have largely improved the performance of natural language processing tasks, including semantic matching [12,22] and question answering. Deep-learning-based methods [4,17,23,32] are consequently introduced for person-job fit, focusing on learning effective representations of the free text of the job post and resume. The learned representations are then compared to generate a matching score.…”