Skip to main content
SpringerLink
Log in

AIRM: A New AI Recruiting Model for the Saudi Arabia Labor Market

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 296))

Included in the following conference series:

Abstract

One of Saudi Arabia’s vision 2030 goals is to keep the unemployment rate at the lowest level to empower the economy. Research has shown that a rise in unemployment has a negative effect on any countries gross domestic product. Artificial Intelligence is the fastest developing technology these days. It has served in many specialties. Recently, Artificial Intelligence technology has shined in the field of recruiting. Researchers are working to invest its capabilities with many applications that help speed up the recruiting process. However, having an open labor market without a coherent data center makes it hard to monitor, integrate, analyze, and build an evaluation matrix that helps reach the best match of job candidate to job vacancy. A recruiter’s job is to assess a candidate’s data to build metrics that can make them choose a suitable candidate. Job seekers build themselves metrics to compare job offers to choose the best opportunity for their preferred choice. This paper address how Artificial Intelligence techniques can be effectively exploited to improve the current Saudi labor market. It aims to decrease the gap between recruiters and job seekers. This paper analyzes the current Saudi labor market, it then outlines an approach that proposes: 1) a new data storage technology approach, and 2) a new Artificial Intelligence architecture, with three layers to extract relevant information from data of both recruiters and job seekers by exploiting machine learning, in particular clustering algorithms, to group data points, natural language processing to convert text to numerical representations, and recurrent neural networks to produce matching keywords, and equations to generate a similarity score. We have completed the current Saudi labor market analysis, and a proposal for the Artificial Intelligence and data storage components is articulated in this paper. The proposed approach and technology will empower the Saudi government’s immediate and strategic decisions by having a comprehensive insight into the labor market.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Saudization is the newest policy of the Kingdom of Saudi Arabia implemented by its Ministry of Labor and Social Development, whereby Saudi companies and enterprises are required to fill up their workforce with Saudi nationals up to certain levels.

References

  1. Nikolaou, I., Oostro, J.K., Employee Recruitment, Selection, And Assessment: Contemporary Issues For Theory And Practice. Psychology Press, Hove, East Sussex (2015)

    Google Scholar 

  2. Breaugh, J.A.: Recruiting and attracting talent. SHRM Foundation, United States of America (2009)

    Google Scholar 

  3. Kenton, W.: Okun’s Law. Investopedia. https://www.investopedia.com/terms/o/okunslaw.asp (26 Mar 2020). Accessed 07 Dec 2020

  4. Saudi vision: Governance Model For Achieving Saudi Arabia’s Vision 2030. https://www.vision2030.gov.sa/ar/node (12 12 2019). Accessed 7 Dec 2020

  5. Alsabeeh, H., Aljassim, A., Ahmed, M., Hagemann, F.: Labor Market Dynamics in the GCC States. OxGAPS Oxford Gulf & Arabian Peninsula Studies Forum, Oxford (2015)

    Google Scholar 

  6. Gill, K., Scott, E., Ward, L.: Understanding labour market information. Produced by the Department for Education and Skills, p. 5 (2004)

    Google Scholar 

  7. Ehrenberg, R.G., Smith, R.S.: Modern Labor Economics, p. 2. Pearson (2012)

    Google Scholar 

  8. Albaker, A., Alabdani, A.: Labor Market Challenges in KSA. Saudi Arabian Monetary Agency, in Arabic (2018)

    Google Scholar 

  9. Wowczko, I.A.: Skills and vacancy analysis with data mining techniques. Open Access Inf. 2, 31–49 (2015)

    Google Scholar 

  10. Al-Zughaibi, S.A.: The Importance of Labor Market Reforms in Restructuring the Saudi Economy, in Arabic. https://www.alriyadh.com/915519 (5 5 2014). Accessed 7 Dec 2020

  11. Greenwood, A.M.: International definitions and prospects of underemployment statistics. In: Proceedings for the Seminario sobre Subempleo, pp. 8–12 (1999)

    Google Scholar 

  12. Stephen, H.: Is it possible to revive the labor market? King Faisal Center for Research and Islamic Studies, Riyadh (2018)

    Google Scholar 

  13. AlOmari, A.: Characteristics of the Labor Market in Saudi Arabia. Economic writer Abdul Hamid Al-Omari, in Arabic. http://abdulhamid.net/archives/3450 (30 09 2003). Accessed 7 Dec 2020

  14. Al-Sudairy, M.A.: Launching the National Labor Observatory Portal to Stimulate Emiratization and the Organization of the Labor Market. Saudi press agency, in Arabic. https://www.spa.gov.sa/1880625 (31 1 2019). Accessed 7 Dec 2020

  15. Statistics, G.O.: Labor Market 3Q 2018. General Organization for Statistics Labor Force Statistics and Social Conditions. Riyadh, in Arabic (2018)

    Google Scholar 

  16. Altorki, F., AlEshakh, R.: Future Features of the Saudi Labor Market. Jadwa investment, Riyadh, in Arabic (2015)

    Google Scholar 

  17. G. a. f. statistics: genral authority for statistics Labor market statistics second quarter of 2020. Genral authority for statistics, Riyadh, in Arabic (2020)

    Google Scholar 

  18. Alsharif, M.: Specialists: 15 percentage of fake Emiratisation in the private sector. https://tinyurl.com/y2rysjud (12 9 2018). Accessed 7 Dec 2020

  19. Sundsy, P., Bjelland, J., Reme, B.: Towards real-time prediction of unemployment and profession. Telenor Group Research, Fornebu, Norway (2019)

    Google Scholar 

  20. SDAIA: About SDAIA. SDAIA, in Arabic (1 3 2019). Accessed 7 Dec 2020

    Google Scholar 

  21. Zomay, A., Sakr, S.: Encyclopedia of big data technologies. https://link.springer.com/referenceworkentry/10.1007%2F978-3-319-63962-8_7-1#howtocite (01 June 2018)

  22. Fang, H.: Managing data lakes in big data era: what’s a data lake and why has it became popular in data management ecosystem. In: The 5th Annual IEEE International Conference on Cyber Technology in Automation, Shenyang, China (2015)

    Google Scholar 

  23. Khine, P.P., Wang, Z.S.: Data lake: a new ideology in big data Era. Researchgate (15 Dec 2017)

    Google Scholar 

  24. Tolstoy, A., Miloslavskaya, N.: Big data, fast data and data lake concepts. Procedia Comput. Sci. 302, 300–305 (2016)

    Google Scholar 

  25. Llave, M.R.: Data lakes in business intelligence: reporting from the trenches. Procedia Comput. Sci. 138, 516–524 (2018)

    Google Scholar 

  26. Patel, P., Diaz, A.: Data lake governance best practices. https://dzone.com/articles/data-lake-governance-best-practices 25 Apr 2017

  27. Stein, B., Morrison, A.: The enterprise data lake: Better integration and deeper analytics. Technol. Forecast: Rethinking Integr. 1, 18 (2014)

    Google Scholar 

  28. EDUCBA: Data Lake Architecture. EDUCBA. https://www.educba.com/data-lake-architecture/. Accessed 7 Dec 2020

  29. Marionoioso: a big data architecture in data processing. https://marionoioso.com/2019/08/22/a-big-data-architecture-in-data-processing/ 22 8 2019

  30. Brownlee, J.: 10 clustering algorithms with python. Machine Learning Mastery. https://machinelearningmastery.com/clustering-algorithms-with-python/ (20 08 2020). Accessed 7 Dec 2020

  31. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: International Conference on Management of Data, vol. 25.2, p. 103 (1996)

    Google Scholar 

  32. Chopra, A., Prashar, A., Sain, C.: Natural language processing. Int. J. Technol. Enhancements Emerg. Eng. Res. 1(4), 131–134 (2013)

    Google Scholar 

  33. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space, vol. 3. arXiv, arXiv:1301.3781 (2013)

  34. Borges, L., Martins, B., Calado, P.: Combining similarity features and deep representation learning for stance detection in the context of checking fake news. ACM J. Data Inf. Qual. 9(4), 39 (2019)

    Google Scholar 

  35. Sutskever, I., Martens, J., Hinton, G.: Generating text with recurrent neural networks. In: Proceedings of the 28th International Conference on Machine Learning, pp. 1017–1014 (2011)

    Google Scholar 

  36. Ha-Thuc, V., Venkataraman, G., Rodriguez, M., Sinha, S., Sundaram, S., Guo, L.: Personalized expertise search at LinkedIn, [cs.IR]. arXiv, arXiv:1602.04572v1 (2016)

  37. Jarvelin, K., AL¨ Ainen, J.K.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422–446 (2002)

    Google Scholar 

  38. Cem Geyik, S., et al.: Talent search and recommendation systems at LinkedIn: practical challenges and lessons learned, [cs.AI]. arXiv, arXiv:1809.06481v1 (2018)

  39. Pathak, M.: Hierarchical clustering in R – DataCamp. DataCamp. https://www.datacamp.com/community/tutorials/hierarchical-clustering-R#what (24 July 2018). Accessed 6 Feb 2021

  40. Sharma, P.: Hierarchical clustering | hierarchical clustering python. Analyticsvidhya. https://www.analyticsvidhya.com/blog/2019/05/beginners-guide-hierarchical-clustering/ 27 May 2019. Accessed 06 Feb 2021

Download references

Author information

Authors and Affiliations

  1. University of Sussex, Falmer, Brighton, BN1 9RH, UK

    Monirah Ali Aleisa, Natalia Beloff & Martin White

Authors
  1. Monirah Ali Aleisa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natalia Beloff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin White
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monirah Ali Aleisa .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aleisa, M.A., Beloff, N., White, M. (2022). AIRM: A New AI Recruiting Model for the Saudi Arabia Labor Market. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_8

Download citation

Publish with us

Policies and ethics

Search

Navigation