Split the cash from cache-friendly recommendations

Abstract

Recommender systems have been established as a key component of video streaming services, shaping up to 80% of content requests. Hence, recommendations are employed by the Content Providers (CPs) of these services to increase the viewing time and their revenues. Furthermore, it has been recently suggested that recommendations could be a means to reduce the operational costs of the Content Delivery Networks (CDNs) when they are related to already cached items, i.e., when they are cache-friendly. Clearly, these conflicting objectives, i.e., increasing revenue for the CP and reducing costs for the CDN, can create tensions between the two entities, and hence, prevent the full utilization of recommendations. In this work, we propose a model for capturing these tradeoffs, and an economic mechanism, based on the Nash bargaining solution, for reconciling the potentially conflicting objectives of the CP and the CDN. Our scheme enables the CP and CDN to jointly design the recommendations in a way that balances the revenue gains and cost savings, ensuring a fair and Pareto optimal split of the accrued benefits for both entities. Our numerical experiments in realistic scenarios show that the proposed scheme leads to important financial gains of up to 30%.