mlsanityData to 7 Oct 2026

Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness

arXiv 1907.13158 ↗First posted 30 Jul 2019cs.IRcited by 19

Not on the map. Why?

Abstract (arXiv)

There is growing research interest in recommendation as a multi-stakeholder problem, one where the interests of multiple parties should be taken into account. This category subsumes some existing well-established areas of recommendation research including reciprocal and group recommendation, but a detailed taxonomy of different classes of multi-stakeholder recommender systems is still lacking. Fairness-aware recommendation has also grown as a research area, but its close connection with multi-stakeholder recommendation is not always recognized. In this paper, we define the most commonly observed classes of multi-stakeholder recommender systems and discuss how different fairness concerns may come into play in such systems.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 4; 2023: 3; 2024: 2; 2025: 3; 2026: 2.

19 papers.

Sort:

  1. 2021User-oriented Fairness in Recommendationcited by 75
  2. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  3. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  4. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  5. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  6. 2021Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectivenesscited by 12
  7. 2021Towards Fair Recommendation in Two-Sided Platformscited by 8
  8. 2022Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filteringcited by 5
  9. 2024Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challengescited by 4
  10. 2021DiRe Committee : Diversity and Representation Constraints in Multiwinner Electionscited by 3
  11. 2023FairRec: Fairness Testing for Deep Recommender Systemscited by 3
  12. 2023Interpolating Item and User Fairness in Multi-Sided Recommendationscited by 3
7 more
  1. 2024Knowledge Distillation Approaches for Accurate and Efficient Recommender Systemcited by 2
  2. 2025Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designscited by 1
  3. 2022Fairly Allocating Utility in Constrained Multiwinner Electionscited by 0
  4. 2025OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender Systemcited by 0
  5. 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systemscited by 0
  6. 2026Aligning Recommendations with User Popularity Preferencescited by 0
  7. 2026FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalizationcited by 0