Patterns of Multistakeholder Recommendation
It addresses the challenge of balancing diverse stakeholder interests in recommendation algorithms, which is incremental as it builds on existing multistakeholder concepts.
The paper tackles the problem of recommender systems needing to represent multiple stakeholders beyond just end-users, and provides a taxonomy of multistakeholder recommendation patterns and systems, noting that only some have been implemented.
Recommender systems are personalized information systems. However, in many settings, the end-user of the recommendations is not the only party whose needs must be represented in recommendation generation. Incorporating this insight gives rise to the notion of multistakeholder recommendation, in which the interests of multiple parties are represented in recommendation algorithms and evaluation. In this paper, we identify patterns of stakeholder utility that characterize different multistakeholder recommendation applications, and provide a taxonomy of the different possible systems, only some of which have currently been implemented.