# A challenge-based survey of e-recruitment recommendation systems

arXiv: https://arxiv.org/abs/2209.05112 · HTML: https://mlsanity.com/paper/2209.05112/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2022.

## Built on by (most cited first, 9 of 9)

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- [FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources](https://mlsanity.com/paper/2311.04542/) (2023)
- [Structurally Refined Graph Transformer for Multimodal Recommendation](https://mlsanity.com/paper/2511.00584/) (2025)
- [A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments](https://mlsanity.com/paper/2603.21699/) (2026)
- [Synapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM-guided Genetic Resume Optimization](https://mlsanity.com/paper/2604.02539/) (2026)
- [EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI](https://mlsanity.com/paper/2610.16689/) (2026)

## Abstract (arXiv)

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences. Therefore, e-recruitment recommendation systems could greatly impact job seekers' careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the companies' competitive edge in the market. Hence, the domain of e-recruitment recommendation deserves specific attention. Existing surveys on this topic tend to 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, which we believe might be more practical to developers facing a concrete e-recruitment design task with a specific set of challenges, as well as to researchers looking for impactful research projects in this domain. 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 promising in the e-recruitment recommendation domain.