  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.
