Job2Vec : Job Title Benchmarking with Collective Multi-View Representation Learning

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

28 Scopus Citations
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Author(s)

  • Denghui Zhang
  • Hengshu Zhu
  • Yanchi Liu
  • Lichen Wang
  • Pengyang Wang
  • Hui Xiong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationCIKM '19
Subtitle of host publicationProceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2763-2771
ISBN (Print)978-1-4503-6976-3
Publication statusPublished - Nov 2019

Conference

TitleCIKM '19
PlaceChina
CityBeijing
Period3 - 7 November 2019

Abstract

Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for both talent recruitment and job seekers for position and salary calibration/prediction. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor intensive. Recently, the rapid development of Online Professional graph has accumulated a large number of talent career records, which provides a promising trend for data-driven solutions. However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for a same position (\eg,Programmer, Software Development Engineer, SDE, Implementation Engineer ), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness for modeling job transition patterns. To overcome these challenges, we aggregate all the records to construct a large-scale Job Title Benchmarking Graph (Job-Graph), where nodes denote job titles affiliated with specific companies and links denote the correlations between jobs. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links. Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view (the structure of relationships among job titles), (2) semantic view (semantic meaning of job descriptions), (3) job transition balance view (the numbers of bidirectional transitions between two similar-level jobs are close), and (4) job transition duration view (the shorter the average duration of transitions is, the more similar the job titles are). We fuse the multi-view representations in the encode-decode paradigm to obtain an unified optimal representations for the task of link prediction. Finally, we conduct extensive experiments to validate the effectiveness of our proposed method.

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning. / Zhang, Denghui; Liu, Junming; Zhu, Hengshu et al.
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2019. p. 2763-2771.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review