Quid pro Quo in Streaming Services: Algorithms for Cooperative Recommendations

TL;DR

This paper develops a cooperative recommendation framework for streaming services and caching networks, balancing user preferences and operational costs through Nash bargaining, leading to fair and optimal gains.

Contribution

It introduces a network-economic model and cooperative policies that align the interests of content providers and caching networks, ensuring fairness and efficiency.

Findings

Cooperative policies improve gains for both CPs and caching networks.

The framework guarantees Pareto optimal and fair resource allocation.

Numerical experiments show significant performance improvements.

Abstract

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.