Recommendations are employed by Content Providers (CPs) of streaming services in order to boost user engagement and their revenues. Recent works suggest that nudging recommendations towards cached items can reduce operational costs in the caching networks, e.g., Content Delivery Networks (CDNs) or edge cache providers in future wireless networks. However, cache-friendly recommendations could deviate from users' tastes, and potentially affect the CP's revenues. Motivated by real-world business models, this work identifies the misalignment of the financial goals of the CP and the caching network provider, and presents a network-economic framework for recommendations. We propose a cooperation mechanism leveraging the Nash bargaining solution that allows the two entities to jointly design the recommendation policy. We consider different problem instances that vary on the extent these entities are willing to share their cost and revenue models, and propose two cooperative policies, CCR and DCR, that allow them to make decisions in a centralized or distributed way. In both cases, our solution guarantees reaching a fair and Pareto optimal allocation of the cooperation gains. Moreover, we discuss the extension of our framework towards caching decisions. A wealth of numerical experiments in realistic scenarios show the policies lead to significant gains for both entities.

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%.