An approximation algorithm for joint caching and recommendations in cache networks
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Streaming platforms, like Netflix and YouTube, strive to offer a high quality of service (QoS) to their users. Meanwhile, a significant share of content consumption of these platforms is heavily influenced by recommendations. In this setting, user experience is a product of both the quality of the recommendations (QoR) and the quality of service (QoS) of the delivered content. However, network decisions (like caching) that affect QoS are usually made without taking into account the recommender's actions. Likewise, recommendation decisions are made independently of the potential delivery quality of the recommended content. The aim of this paper is to jointly optimize caching and recommendations in a generic network of caches, with the objective of maximizing the quality of experience (QoE). This is in line with the recent trend for large content providers to simultaneously act as Content Delivery Network (CDN) owners. We formulate this joint optimization problem and prove that it can be approximated up to a constant. We believe this to be the first polynomial algorithm to achieve a constant approximation ratio for the joint problem. Moreover, our numerical experiments show important performance gains of our algorithm over baseline schemes and existing algorithms in the literature.
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