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Multi-Stakeholder Recommendation: Applications and Challenges
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Recommender systems have been successfully applied to assist decision making by producing a list of item recommendations tailored to user preferences. Traditional recommender systems only focus on optimizing the utility of the end users who are the receiver of the recommendations. By contrast, multi-stakeholder recommendation attempts to generate recommendations that satisfy the needs of both the end users and other parties or stakeholders. This paper provides an overview and discussion about the multi-stakeholder recommendations from the perspective of practical applications, available data sets, corresponding research challenges and potential solutions.
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Cited by 1 Pith paper
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Equity by Design? On the Trade-Offs in Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets
The 'free fairness' result for producer constraints vanishes for multi-item recommendations; a CVaR group-fairness objective and business constraints can be added with moderate trade-offs.
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