research-article

Salience and Market-aware Skill Extraction for Job Targeting

Authors:

Qi HeAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2871 - 2879

https://doi.org/10.1145/3394486.3403338

Published: 20 August 2020 Publication History

Abstract

At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs You May Be Interested In (JYMBII) system to match the best candidates with their dream jobs. One of the most challenging tasks for developing these systems is to properly extract important skill entities from job postings and then target members with matched attributes. In this work, we show that the commonly used text-based salience and market-agnostic skill extraction approach is sub-optimal because it only considers skill mention and ignores the salient level of a skill and its market dynamics, i.e., the market supply and demand influence on the importance of skills. To address the above drawbacks, we present Job2Skills, our deployed salience and market-aware skill extraction system. The proposed Job2Skills shows promising results in improving the online performance of job recommendation (JYMBII) (+1.92% job apply) and skill suggestions for job posters (-37% suggestion rejection rate). Lastly, we present case studies to show interesting insights that contrast traditional skill recognition method and the proposed Job2Skills from occupation, industry, country, and individual skill levels. Based on the above promising results, we deployed the Job2Skills online to extract job targeting skills for all 20M job postings served at LinkedIn.

Supplementary Material

MP4 File (3394486.3403338.mp4)

Presentation video

Download
199.41 MB

References

[1]

Fabian Abel. 2015. We know where you should work next summer: job recommendations. In RecSys.

[2]

Fabian Abel, Yashar Deldjoo, Mehdi Elahi, and Daniel Kohlsdorf. 2017. Recsys challenge 2017: Offline and online evaluation. In RecSys.

[3]

Deepak Agarwal, Bee-Chung Chen, and Pradheep Elango. 2009. Spatio-temporal models for estimating click-through rate. In WWW.

[4]

David H Autor, Frank Levy, and Richard J Murnane. 2003. The skill content of recent technological change: An empirical exploration. The Quarterly journal of economics, Vol. 118, 4 (2003).

[5]

Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. TACL, Vol. 5 (2017).

[6]

Fedor Borisyuk, Liang Zhang, and Krishnaram Kenthapadi. 2017. LiJAR: A system for job application redistribution towards efficient career marketplace. In KDD.

[7]

Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et almbox. 2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018).

[8]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In KDD.

Digital Library

[9]

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et almbox. 2016. Wide & deep learning for recommender systems. In DLRS.

[10]

National Research Council et almbox. 2010. A database for a changing economy: Review of the Occupational Information Network (O* NET).

[11]

Vachik S Dave, Baichuan Zhang, Mohammad Al Hasan, Khalifeh AlJadda, and Mohammed Korayem. 2018. A combined representation learning approach for better job and skill recommendation. In CIKM.

[12]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[13]

Yupeng Gu, Bo Zhao, David Hardtke, and Yizhou Sun. 2016. Learning global term weights for content-based recommender systems. In WWW.

[14]

Cheng Guo, Hongyu Lu, Shaoyun Shi, Bin Hao, Bin Liu, Min Zhang, Yiqun Liu, and Shaoping Ma. 2017. How Integration helps on Cold-Start Recommendations. In RecSys Challenge 2017.

Digital Library

[15]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW.

[16]

Chao Huang, Xian Wu, Xuchao Zhang, Chuxu Zhang, Jiashu Zhao, Dawei Yin, and Nitesh V Chawla. 2019. Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics. In KDD.

[17]

Brian Johnston, Benjamin Zweig, Michael Peran, Charlie Wang, and Rachel Rosenfeld. 2017. Estimating skill fungibility and forecasting services labor demand. In Big Data.

[18]

Krishnaram Kenthapadi, Benjamin Le, and Ganesh Venkataraman. 2017. Personalized job recommendation system at linkedin: Practical challenges and lessons learned. In RecSys.

[19]

Jia Li, Dhruv Arya, Viet Ha-Thuc, and Shakti Sinha. 2016. How to get them a dream job?: Entity-aware features for personalized job search ranking. In KDD.

[20]

Liangyue Li, How Jing, Hanghang Tong, Jaewon Yang, Qi He, and Bee-Chung Chen. 2017. Nemo: Next career move prediction with contextual embedding. In WWW.

Digital Library

[21]

Shan Li, Baoxu Shi, Jaewon Yang, Ji Yan, Shuai Wang, Fei Chen, and Qi He. 2020. Deep Job Understanding at LinkedIn. In SIGIR.

[22]

Yin Lou and Mikhail Obukhov. 2017. BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency. In KDD.

Digital Library

[23]

Qingxin Meng, Hengshu Zhu, Keli Xiao, Le Zhang, and Hui Xiong. 2019. A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction. In KDD. ACM.

[24]

David Nadeau and Satoshi Sekine. 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes, Vol. 30, 1 (2007).

[25]

Ioannis Paparrizos, B Barla Cambazoglu, and Aristides Gionis. 2011. Machine learned job recommendation. In RecSys.

[26]

Norman G Peterson, Michael D Mumford, Walter C Borman, P Richard Jeanneret, Edwin A Fleishman, Kerry Y Levin, Michael A Campion, Melinda S Mayfield, Frederick P Morgeson, Kenneth Pearlman, et almbox. 2001. Understanding work using the Occupational Information Network (O* NET): Implications for practice and research. Personnel Psychology, Vol. 54, 2 (2001).

[27]

Bipin Prabhakar, Charles R Litecky, and Kirk Arnett. 2005. IT skills in a tough job market. Commun. ACM, Vol. 48, 10 (2005).

Digital Library

[28]

Chuan Qin, Hengshu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, and Hui Xiong. 2018. Enhancing person-job fit for talent recruitment: An ability-aware neural network approach. In SIGIR.

[29]

Chuan Qin, Hengshu Zhu, Chen Zhu, Tong Xu, Fuzhen Zhuang, Chao Ma, Jingshuai Zhang, and Hui Xiong. 2019. DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview. In KDD.

[30]

Alex Radermacher, Gursimran Walia, and Dean Knudson. 2014. Investigating the skill gap between graduating students and industry expectations. In ICSE.

[31]

Rohan Ramanath, Hakan Inan, Gungor Polatkan, Bo Hu, Qi Guo, Cagri Ozcaglar, Xianren Wu, Krishnaram Kenthapadi, and Sahin Cem Geyik. 2018. Towards Deep and Representation Learning for Talent Search at LinkedIn. In CIKM. 2253--2261.

[32]

Ellu Saar and Mari Liis Räis. 2017. Participation in job-related training in European countries: the impact of skill supply and demand characteristics. Journal of Education and Work, Vol. 30, 5 (2017).

[33]

Baoxu Shi, Shan Li, Jaewon Yang, Mustafa Emre Kazdagli, and Qi He. 2020. Learning to Ask Screening Questions for Job Postings. In SIGIR.

[34]

Baoxu Shi, Jaewon Yang, Tim Weninger, Jing How, and Qi He. 2019. Representation Learning in Heterogeneous Professional Social Networks with Ambiguous Social Connections. In IEEE BigData.

[35]

Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Xin Song, Qing He, and Hui Xiong. 2019. The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach. In KDD.

[36]

Shrihari Vasudevan, Moninder Singh, Joydeep Mondal, Michael Peran, Benjamin Zweig, Brian Johnston, and Rachel Rosenfeld. 2018. Estimating Fungibility Between Skills by Combining Skill Similarities Obtained from Multiple Data Sources. Data Science and Engineering, Vol. 3, 3 (2018).

[37]

Maksims Volkovs, Guang Wei Yu, and Tomi Poutanen. 2017. Content-based Neighbor Models for Cold Start in Recommender Systems. In RecSys.

Digital Library

[38]

Wei Lee Woon, Zeyar Aung, Wala AlKhader, Davor Svetinovic, and Mohammad Atif Omar. 2015. Changes in occupational skills-a case study using non-negative matrix factorization. In ICONIP.

[39]

Xunxian Wu, Tong Xu, Hengshu Zhu, Le Zhang, Enhong Chen, and Hui Xiong. 2019. Trend-aware tensor factorization for job skill demand analysis. In IJCAI.

[40]

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, and Liefeng Bo. 2020. Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network. In SIGIR.

[41]

Tong Xu, Hengshu Zhu, Chen Zhu, Pan Li, and Hui Xiong. 2018. Measuring the popularity of job skills in recruitment market: A multi-criteria approach. In AAAI.

[42]

Xiao Yan, Jaewon Yang, Mikhail Obukhov, Lin Zhu, Joey Bai, Shiqi Wu, and Qi He. 2019. Social Skill Validation at LinkedIn. In KDD.

[43]

Yuyang Ye, Hengshu Zhu, Tong Xu, Fuzhen Zhuang, Runlong Yu, and Hui Xiong. 2019. Identifying High Potential Talent: A Neural Network Based Dynamic Social Profiling Approach. In ICDM.

[44]

XianXing Zhang, Yitong Zhou, Yiming Ma, Bee-Chung Chen, Liang Zhang, and Deepak Agarwal. 2016. Glmix: Generalized linear mixed models for large-scale response prediction. In KDD.

Digital Library

[45]

Chen Zhu, Hengshu Zhu, Hui Xiong, Chao Ma, Fang Xie, Pengliang Ding, and Pan Li. 2018. Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning. TMIS, Vol. 9, 3 (2018).

Digital Library

Cited By

Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659942
Zha RSun YQin CZhang LXu TZhu HChen E(2024)Towards Unified Representation Learning for Career Mobility Analysis with Trajectory HypergraphACM Transactions on Information Systems10.1145/365115842:4(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3651158
Yamashita MTran TLee DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Fake Resume Attacks: Data Poisoning on Online Job PlatformsProceedings of the ACM Web Conference 202410.1145/3589334.3645524(1734-1745)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645524
Show More Cited By

Index Terms

Salience and Market-aware Skill Extraction for Job Targeting
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
      1. Information extraction
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction
      2. Recommender systems

Recommendations

Cost-Effective and Interpretable Job Skill Recommendation with Deep Reinforcement Learning
WWW '21: Proceedings of the Web Conference 2021

Nowadays, as organizations operate in very fast-paced and competitive environments, workforce has to be agile and adaptable to regularly learning new job skills. However, it is nontrivial for talents to know which skills to develop at each working ...
A Combined Representation Learning Approach for Better Job and Skill Recommendation
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Job recommendation is an important task for the modern recruitment industry. An excellent job recommender system not only enables to recommend a higher paying job which is maximally aligned with the skill-set of the current job, but also suggests to ...
The IT/IS job market: a longitudinal perspective
SIGMIS CPR '06: Proceedings of the 2006 ACM SIGMIS CPR conference on computer personnel research: Forty four years of computer personnel research: achievements, challenges & the future

This paper describes parallels between the IS/IT job market of the 1990's to that of the current decade using longitudinal data collected for these periods from job position ads. Although the data exhibits some measurement issues, it offers insight into ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 ACM.

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 the author(s) 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 [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '20

Sponsor:

KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

July 6 - 10, 2020

CA, Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
669
Total Downloads

Downloads (Last 12 months)77
Downloads (Last 6 weeks)4

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mashayekhi YLi NKang BLijffijt JDe Bie T(2024)A Challenge-based Survey of E-recruitment Recommendation SystemsACM Computing Surveys10.1145/365994256:10(1-33)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3659942
Zha RSun YQin CZhang LXu TZhu HChen E(2024)Towards Unified Representation Learning for Career Mobility Analysis with Trajectory HypergraphACM Transactions on Information Systems10.1145/365115842:4(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3651158
Yamashita MTran TLee DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Fake Resume Attacks: Data Poisoning on Online Job PlatformsProceedings of the ACM Web Conference 202410.1145/3589334.3645524(1734-1745)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645524
Liu SXu YMa S(2024)Hypergraph-Enhanced Self-Supervised Robust Graph Learning for Social RecommendationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448250(5545-5549)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10448250
Akkasi A(2024)Job description parsing with explainable transformer based ensemble models to extract the technical and non-technical skillsNatural Language Processing Journal10.1016/j.nlp.2024.1001029(100102)Online publication date: Dec-2024
https://doi.org/10.1016/j.nlp.2024.100102
Chen HMason CWang QZhao Y(2024)DBSSM: Deep BERT-Based Semantic Skill Matching from Resumes to a Public Skill TaxonomyAI 2024: Advances in Artificial Intelligence10.1007/978-981-96-0348-0_23(316-328)Online publication date: 18-Nov-2024
https://doi.org/10.1007/978-981-96-0348-0_23
Saito YSugiyama K(2023)Multi-Behavior Job Recommendation with Dynamic AvailabilityProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625314(264-271)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625314
Qin CZhu HShen DSun YYao KWang PXiong H(2023)Automatic Skill-Oriented Question Generation and Recommendation for Intelligent Job InterviewsACM Transactions on Information Systems10.1145/360455242:1(1-32)Online publication date: 13-Jun-2023
https://dl.acm.org/doi/10.1145/3604552
Yamashita MShen JTran TEkhtiari HLee D(2023)JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302559(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302559
Guo ZLiu HZhang LZhang QZhu HXiong HZhang ARangwala H(2022)Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-LearningProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539139(2957-2967)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539139
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents