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A challenge-based survey of e-recruitment recommendation systems

Yoosof Mashayekhi (UGent) , Nan Li (UGent) , Bo Kang (UGent) , Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)
(2024) ACM COMPUTING SURVEYS. 56(10).
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Abstract
E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the competitive edge of companies. Hence, it seems prudent to consider what (unique) challenges there are for recommendation systems in e-recruitment. Existing surveys on this topic discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content-based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach. We believe this is more practical for developers facing a concrete e-recruitment design task with a specific set of challenges, and also for researchers that look for impactful research projects in this domain. In this survey, we first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider most promising in the e-recruitment recommendation domain.
Keywords
Job recommendation, e-recruitment recommendation, JOB, INFORMATION

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Citation

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MLA
Mashayekhi, Yoosof, et al. “A Challenge-Based Survey of e-Recruitment Recommendation Systems.” ACM COMPUTING SURVEYS, vol. 56, no. 10, 2024, doi:10.1145/3659942.
APA
Mashayekhi, Y., Li, N., Kang, B., Lijffijt, J., & De Bie, T. (2024). A challenge-based survey of e-recruitment recommendation systems. ACM COMPUTING SURVEYS, 56(10). https://doi.org/10.1145/3659942
Chicago author-date
Mashayekhi, Yoosof, Nan Li, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2024. “A Challenge-Based Survey of e-Recruitment Recommendation Systems.” ACM COMPUTING SURVEYS 56 (10). https://doi.org/10.1145/3659942.
Chicago author-date (all authors)
Mashayekhi, Yoosof, Nan Li, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2024. “A Challenge-Based Survey of e-Recruitment Recommendation Systems.” ACM COMPUTING SURVEYS 56 (10). doi:10.1145/3659942.
Vancouver
1.
Mashayekhi Y, Li N, Kang B, Lijffijt J, De Bie T. A challenge-based survey of e-recruitment recommendation systems. ACM COMPUTING SURVEYS. 2024;56(10).
IEEE
[1]
Y. Mashayekhi, N. Li, B. Kang, J. Lijffijt, and T. De Bie, “A challenge-based survey of e-recruitment recommendation systems,” ACM COMPUTING SURVEYS, vol. 56, no. 10, 2024.
@article{01HW7G7289T9WKMA21N7R6Y403,
  abstract     = {{E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the competitive edge of companies. Hence, it seems prudent to consider what (unique) challenges there are for recommendation systems in e-recruitment. Existing surveys on this topic discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content-based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach. We believe this is more practical for developers facing a concrete e-recruitment design task with a specific set of challenges, and also for researchers that look for impactful research projects in this domain. In this survey, we first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider most promising in the e-recruitment recommendation domain.}},
  articleno    = {{252}},
  author       = {{Mashayekhi, Yoosof and Li, Nan and Kang, Bo and Lijffijt, Jefrey and De Bie, Tijl}},
  issn         = {{0360-0300}},
  journal      = {{ACM COMPUTING SURVEYS}},
  keywords     = {{Job recommendation,e-recruitment recommendation,JOB,INFORMATION}},
  language     = {{eng}},
  number       = {{10}},
  pages        = {{33}},
  title        = {{A challenge-based survey of e-recruitment recommendation systems}},
  url          = {{http://doi.org/10.1145/3659942}},
  volume       = {{56}},
  year         = {{2024}},
}
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