Incorporating System-Level Objectives into Recommender Systems
This addresses the need to balance multiple stakeholder interests in recommender systems, but it appears incremental as it builds on existing optimization techniques.
The paper tackles the problem of multistakeholder recommendation, focusing on algorithms where the recommender system itself is a stakeholder, and explores incremental incorporation of system-level objectives to address issues in optimizing only individual user lists.
One of the most essential parts of any recommender system is personalization-- how acceptable the recommendations are from the user's perspective. However, in many real-world applications, there are other stakeholders whose needs and interests should be taken into account. In this work, we define the problem of multistakeholder recommendation and we focus on finding algorithms for a special case where the recommender system itself is also a stakeholder. In addition, we will explore the idea of incremental incorporation of system-level objectives into recommender systems over time to tackle the existing problems in the optimization techniques which only look for optimizing the individual users' lists.