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dc.contributor.authorYan, Rui
dc.contributor.authorZhang, Tao
dc.contributor.authorLe, Ran
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorSong, Yang
dc.contributor.authorZhao, Dongyan
dc.date.accessioned2019-09-17T06:26:30Z
dc.date.available2019-09-17T06:26:30Z
dc.date.issued2019-07-26
dc.identifier.citationYan, R., Le, R., Song, Y., Zhang, T., Zhang, X., & Zhao, D. (2019). Interview Choice Reveals Your Preference on the Market. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3292500.3330963
dc.identifier.doi10.1145/3292500.3330963
dc.identifier.urihttp://hdl.handle.net/10754/656763
dc.description.abstractOnline recruitment services are now rapidly changing the landscape of hiring traditions on the job market. There are hundreds of millions of registered users with resumes, and tens of millions 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 job-resume matching performance against a series of state-of-the-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.
dc.description.sponsorshipWe thank the reviewers for their valuable comments. This work was supported by the National Key Research and Development Program of China (No. 2017YFC0804001), the National Science Foundation of China (NSFC No. 61876196, NSFC No. 61828302, and NSFC No. 61672058).
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=3292500.3330963
dc.rightsArchived with thanks to the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
dc.subjectJob-resume matching
dc.subjecttalent recruitment
dc.subjectprofiling memory
dc.subjectneural networks
dc.titleInterview choice reveals your preference on the market: To improve job-resume matching through profiling memories
dc.typeConference Paper
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.date2019-08-04 to 2019-08-08
dc.conference.name25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
dc.conference.locationAnchorage, AK, USA
dc.eprint.versionPost-print
dc.contributor.institutionInstitute of Computer Science and Technology, Peking University, Beijing, China
dc.contributor.institutionBoss Zhipin, Beijing, China
dc.contributor.institutionCenter for Data Science, AAIS, Peking University, Beijing, China
kaust.personZhang, Xiangliang
refterms.dateFOA2019-09-17T12:20:48Z
dc.date.published-online2019-07-26
dc.date.published-print2019


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