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.

翻译：内容提供商（CP）通过流媒体服务中的推荐来提升用户参与度和自身收益。近期研究表明，将推荐导向缓存内容可降低缓存网络（例如内容分发网络（CDN）或未来无线网络中的边缘缓存提供商）的运营成本。然而，缓存友好的推荐可能偏离用户偏好，并可能影响CP的收益。受现实世界商业模式的启发，本文识别了CP与缓存网络提供商财务目标的不一致性，并提出了一种用于推荐的网络经济框架。我们提出了一种基于纳什谈判解的合作机制，使双方能够共同设计推荐策略。我们考虑了不同的问题实例，这些实例中双方愿意共享其成本和收益模型的程度有所不同，并提出了两种合作策略——CCR和DCR，使它们能够以集中式或分布式方式做出决策。在这两种情况下，我们的解决方案均保证了合作收益的公平且帕累托最优的分配。此外，我们讨论了将框架扩展到缓存决策的可能性。基于现实场景的大量数值实验表明，这些策略为双方带来了显著的收益增长。