# Explainable Multi-Stakeholder Job Recommender Systems

arXiv: https://arxiv.org/abs/2410.00654 · HTML: https://mlsanity.com/paper/2410.00654/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Building Human Values into Recommender Systems: An Interdisciplinary Synthesis](https://mlsanity.com/paper/2207.10192/) (2022)
- [Job Recommender Systems: A Review](https://mlsanity.com/paper/2111.13576/) (2021)
- [A Co-design Study for Multi-Stakeholder Job Recommender System Explanations](https://mlsanity.com/paper/2309.05507/) (2023)
- [Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings](https://mlsanity.com/paper/2001.04348/) (2019)

## Built on by (most cited first, 2 of 2)

- [Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs](https://mlsanity.com/paper/2509.09522/) (2025)
- [An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation](https://mlsanity.com/paper/2605.24155/) (2026)

## Abstract (arXiv)

Public opinion on recommender systems has become increasingly wary in recent years. In line with this trend, lawmakers have also started to become more critical of such systems, resulting in the introduction of new laws focusing on aspects such as privacy, fairness, and explainability for recommender systems and AI at large. These concepts are especially crucial in high-risk domains such as recruitment. In recruitment specifically, decisions carry substantial weight, as the outcomes can significantly impact individuals' careers and companies' success. Additionally, there is a need for a multi-stakeholder approach, as these systems are used by job seekers, recruiters, and companies simultaneously, each with its own requirements and expectations. In this paper, I summarize my current research on the topic of explainable, multi-stakeholder job recommender systems and set out a number of future research directions.