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Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness
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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.
Forward citations
Cited by 2 Pith papers
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Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations
Combining the context-aware LORE model with calibrated popularity re-ranking yields POI recommendations whose popularity distribution most closely matches users' historical preferences.
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Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?
A comparative literature review shows tourism management and computer science define multistakeholder fairness differently, and argues algorithmic design should adopt qualitative, participatory methods from tourism research.
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