Multi-Stakeholder Recommendation: Applications and Challenges
This work tackles the need for more inclusive recommender systems that balance multiple stakeholder interests, but it is incremental as it reviews existing concepts rather than presenting new methods.
The paper addresses the problem of traditional recommender systems focusing only on end-user utility by introducing multi-stakeholder recommendation, which aims to satisfy both users and other stakeholders, and provides an overview of applications, datasets, challenges, and solutions.
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.