Interview Choice Reveals Your Preference on the Market: To Improve Job-Resume Matching through Profiling Memories

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. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. KDD ’19, August 4–8, 2019, Anchorage, AK, USA © 2019 Association for Computing Machinery. ACM ISBN 978-1-4503-6201-6/19/08. . . $15.00 https://doi.org/10.1145/3292500.3330963 CCS CONCEPTS • Computer systems organization → Embedded systems; Redundancy ; Robotics; • Networks → Network reliability.