# OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System

arXiv: https://arxiv.org/abs/2504.07108 · HTML: https://mlsanity.com/paper/2504.07108/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Lessons on Parameter Sharing across Layers in Transformers](https://mlsanity.com/paper/2104.06022/) (2021)
- [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021)
- [Fairness-Aware Explainable Recommendation over Knowledge Graphs](https://mlsanity.com/paper/2006.02046/) (2020)
- [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017)
- [Poolingformer: Long Document Modeling with Pooling Attention](https://mlsanity.com/paper/2105.04371/) (2021)
- [Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach](https://mlsanity.com/paper/1812.08947/) (2018)
- [Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Survey](https://mlsanity.com/paper/2309.13933/) (2023)
- [Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness](https://mlsanity.com/paper/1907.13158/) (2019)
- [DeText: A Deep Text Ranking Framework with BERT](https://mlsanity.com/paper/2008.02460/) (2020)
- [conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers](https://mlsanity.com/paper/2109.06501/) (2021)
- [Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation](https://mlsanity.com/paper/1910.08288/) (2019)
- [MEGAN: Multi-Explanation Graph Attention Network](https://mlsanity.com/paper/2211.13236/) (2022)
- [JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer](https://mlsanity.com/paper/2404.04313/) (2024)
- [MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels](https://mlsanity.com/paper/2401.12593/) (2024)
- [Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations](https://mlsanity.com/paper/2409.15971/) (2024)

## Abstract (arXiv)

The use of recommender systems in the recruitment domain has been labeled as 'high-risk' in recent legislation. As a result, strict requirements regarding explainability and fairness have been put in place to ensure proper treatment of all involved stakeholders. To allow for stakeholder-specific explainability, while also handling highly heterogeneous recruitment data, we propose a novel explainable multi-stakeholder job recommender system using graph neural networks: the Occupational Knowledge-based Recommender using Attention (OKRA). The proposed method is capable of providing both candidate- and company-side recommendations and explanations. We find that OKRA performs substantially better than six baselines in terms of nDCG for two datasets. Furthermore, we find that the tested models show a bias toward candidates and vacancies located in urban areas. Overall, our findings suggest that OKRA provides a balance between accuracy, explainability, and fairness.