Article: Job recommendation systems and the benefits from the use of Gallup tests Journal: International Journal of Web Engineering and Technology (IJWET) 2026 Vol.21 No.2 pp.195 - 213 Abstract: Job recommendation systems play a vital role in modern recruitment processes by leveraging data analytics and machine learning algorithms to provide personalised and relevant job recommendations to both job seekers and employers. This work examines how job recommendation systems work, focusing on their key components, algorithmic techniques, data sources and evaluation metrics. Furthermore, it explores the potential of these systems on taking advantage of employee profiling services, improving the efficiency of skill matching for individuals, and enhancing the recruitment processes for organisations. Additionally, ethical considerations and real-world examples of job recommendation systems are discussed, shedding light on their effectiveness, limitations and future developments. Finally, the article proposes a novel approach for using employee profile information and more specifically their profile features as described by Gallup tests in conjunction with their CV information and the information of a job posting they are interested in, in order to recommend them which skills to improve and help companies to improve job postings in order to better state the required soft skills. Inderscience Publishers - linking academia, business and industry through research

Title: Job recommendation systems and the benefits from the use of Gallup tests

Authors: Shakhmar Sarsenbay; Iraklis Varlamis; Cemil Turan; Zhadyra Zhalgassova

Addresses: Computer Science Department, SDU University, Almaty, Kazakhstan ' Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece ' Computer Science Department, SDU University, Almaty, Kazakhstan ' Computer Science Department, SDU University, Almaty, Kazakhstan

Abstract: Job recommendation systems play a vital role in modern recruitment processes by leveraging data analytics and machine learning algorithms to provide personalised and relevant job recommendations to both job seekers and employers. This work examines how job recommendation systems work, focusing on their key components, algorithmic techniques, data sources and evaluation metrics. Furthermore, it explores the potential of these systems on taking advantage of employee profiling services, improving the efficiency of skill matching for individuals, and enhancing the recruitment processes for organisations. Additionally, ethical considerations and real-world examples of job recommendation systems are discussed, shedding light on their effectiveness, limitations and future developments. Finally, the article proposes a novel approach for using employee profile information and more specifically their profile features as described by Gallup tests in conjunction with their CV information and the information of a job posting they are interested in, in order to recommend them which skills to improve and help companies to improve job postings in order to better state the required soft skills.

Keywords: job recommendation systems; Gallup tests; soft skills; personality assessment; employee profiling.

DOI: 10.1504/IJWET.2026.155086

International Journal of Web Engineering and Technology, 2026 Vol.21 No.2, pp.195 - 213

Received: 26 Sep 2024
Accepted: 24 Mar 2025
Published online: 27 Jul 2026 *

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Title: Job recommendation systems and the benefits from the use of Gallup tests

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