# Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

arXiv: https://arxiv.org/abs/2006.04282 · HTML: https://mlsanity.com/paper/2006.04282/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 13 of 13)

- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review](https://mlsanity.com/paper/2307.01221/) (2023)
- [OpenClaw AI Agents as Informal Learners at Moltbook: Characterizing an Emergent Learning Community at Scale](https://mlsanity.com/paper/2602.18832/) (2026)
- [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023)
- [Finding Paths for Explainable MOOC Recommendation: A Learner Perspective](https://mlsanity.com/paper/2312.10082/) (2023)
- [Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets](https://mlsanity.com/paper/2504.11504/) (2025)
- [FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation](https://mlsanity.com/paper/2109.05931/) (2021)
- [Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models](https://mlsanity.com/paper/2203.10170/) (2022)
- [Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss And Language Switching Method](https://mlsanity.com/paper/2401.09699/) (2024)
- [MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels](https://mlsanity.com/paper/2401.12593/) (2024)
- [Course Recommender Systems Need to Consider the Job Market](https://mlsanity.com/paper/2404.10876/) (2024)
- [FairExpand: Individual Fairness on Graphs with Partial Similarity Information](https://mlsanity.com/paper/2512.18180/) (2025)

## Abstract (arXiv)

Online educational platforms are playing a primary role in mediating the success of individuals' careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform values, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we formalize educational principles that model recommendations' learning properties, and a novel fairness metric that combines them in order to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a large-scale course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. Our study moves a step forward in operationalizing the ethics of human learning in recommendations, a core unit of intelligent educational systems.