  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.
