# Job Recommender Systems: A Review

arXiv: https://arxiv.org/abs/2111.13576 · HTML: https://mlsanity.com/paper/2111.13576/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2021.

## Built on by (most cited first, 12 of 12)

- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [LinkSAGE: Optimizing Job Matching Using Graph Neural Networks](https://mlsanity.com/paper/2402.13430/) (2024)
- [A Co-design Study for Multi-Stakeholder Job Recommender System Explanations](https://mlsanity.com/paper/2309.05507/) (2023)
- [Explainable Multi-Stakeholder Job Recommender Systems](https://mlsanity.com/paper/2410.00654/) (2024)
- [Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry](https://mlsanity.com/paper/2304.00006/) (2023)
- [Learning to Retrieve for Job Matching](https://mlsanity.com/paper/2402.13435/) (2024)
- [Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations](https://mlsanity.com/paper/2409.15971/) (2024)
- [Remote Work Optimization with Robust Multi-channel Graph Neural Networks](https://mlsanity.com/paper/2209.03150/) (2022)
- [App for Resume-Based Job Matching with Speech Interviews and Grammar Analysis: A Review](https://mlsanity.com/paper/2311.14729/) (2023)
- [A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments](https://mlsanity.com/paper/2603.21699/) (2026)
- [From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance](https://mlsanity.com/paper/2609.04286/) (2026)

## Abstract (arXiv)

This paper provides a review of the job recommender system (JRS) literature published in the past decade (2011-2021). Compared to previous literature reviews, we put more emphasis on contributions that incorporate the temporal and reciprocal nature of job recommendations. Previous studies on JRS suggest that taking such views into account in the design of the JRS can lead to improved model performance. Also, it may lead to a more uniform distribution of candidates over a set of similar jobs. We also consider the literature from the perspective of algorithm fairness. Here we find that this is rarely discussed in the literature, and if it is discussed, many authors wrongly assume that removing the discriminatory feature would be sufficient. With respect to the type of models used in JRS, authors frequently label their method as `hybrid'. Unfortunately, they thereby obscure what these methods entail. Using existing recommender taxonomies, we split this large class of hybrids into subcategories that are easier to analyse. We further find that data availability, and in particular the availability of click data, has a large impact on the choice of method and validation. Last, although the generalizability of JRS across different datasets is infrequently considered, results suggest that error scores may vary across these datasets.