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

Abstract

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.

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Cited by in corpus (9)

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• Beyond Personalization: Research Directions in Multistakeholder Recommendation
• User-oriented Fairness in Recommendation
• Personalized Counterfactual Fairness in Recommendation
• Towards Fair Recommendation in Two-Sided Platforms
• A Reliability-aware Distributed Framework to Schedule Residential Charging of Electric Vehicles
• Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
• Group Fairness in Bandit Arm Selection
• Facets of Fairness in Search and Recommendation