Behaviorally-Based Textual Similarity Engine for Matching Job-Seekers with Jobs

Heggo, Islam A.; Abdelbaki, Nashwa

doi:10.1007/978-3-319-74690-6_55

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Keeping in view candidates' requirements for time-efficient and accurate job searches, researchers have applied a reverse approach of assessing the relevance of jobs for potential candidates and making recommendations accordingly. The dataset used for this kind of assessment varies from a narrow set like the candidates' profile information (Heggo and Abdelbaki, 2018) to a broader set including the information of their network of friends (Diaby et al, 2013).…”

Section: Recruitmentmentioning

confidence: 99%

A review of machine learning applications in human resource management

Garg

Sinha

Kar

et al. 2021

IJPPM

149

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PurposeThis paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).Design/methodology/approachA semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.FindingsThe review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.Originality/valueGiven the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

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Section: Recruitmentmentioning

confidence: 99%

A review of machine learning applications in human resource management

Garg

Sinha

Kar

et al. 2021

IJPPM

149

View full text Add to dashboard Cite

show abstract

“…Research on career selection and recommendations cannot be separated from artificial intelligence (AI) approaches such as text mining [13], [14] [15] and data mining [3], [16]. Research in this field has also discussed matching job profiles with job seeker profiles [17], [18] and selecting appropriate courses [16], [19]. The job level associated with worker experience also covers the job recommendation system as proven by [20].…”

Section: Introductionmentioning

confidence: 99%

Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia

Siswipraptini,

Warnars,

Ramadhan

et al. 2024

IEEE Access

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One of the challenging decisions for students is taking a job specialization. To make their decisions, they use subjective perceptions of friends or family due to the lack of guidance and limited resources. This increases the risk of dissatisfaction with the work environments. To address these drawbacks, this study presents a personalized career-path recommendation model (CPRM) to provide guidance and help college students choose information technology jobs. The design of the CPRM is based on the personalized Naïve Bayes (p-NB) algorithm with three primary sources: job profiles, personality types, and subjects. The association between personality type and college students was established using samples of 104 computer science students enrolled in private universities in Indonesia. CPRM was implemented as a web-based application. This study evaluated the model by measuring the quality of the recommended items to determine whether the proposed model is well accepted by users. The model considers educational data mining grounded theory (EDM-GT) data integration and hierarchically related concepts. CPRM has been validated by Information Technology (IT) professionals and three psychologists in Indonesia through focus group discussions. The evaluation results showed that more than 83% of respondents were satisfied with the recommendation model. Hence, CPRM can provide automatic academic advisors and guidance to computer science students interested in pursuing careers in IT jobs. The result shows that CPRM is the first career path recommendation model based on EDM-GT to target the computer science community in Indonesia.INDEX TERMS Educational data mining, grounded theory, information technology, naïve bayes, career path recommendation model.

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Behaviorally-Based Textual Similarity Engine for Matching Job-Seekers with Jobs

Cited by 2 publications

References 8 publications

A review of machine learning applications in human resource management

A review of machine learning applications in human resource management

Personalized Career-Path Recommendation Model for Information Technology Students in Indonesia

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