User-centric Optimization of Caching and Recommendations in Edge Cache Networks

On streaming platforms such as Youtube and Netflix, recommendations influence a large share of content consumption. In this context, use rexperience depends on both the quality of the recommendations (QoR) and the quality of service (QoS) of the delivered content. However, network decisions (such as caching) affecting QoS are usually made without explicit knowledge of the recommender's actions. Similarly, recommendation decisions are made without considering the potential delivery quality of the recommended content. In this paper, we propose to jointly optimize caching and recommendations in a generic network of caches, towards maximizing the quality of experience (QoE). This coincides with the recent trend for large content providers to also act as Content Delivery Network (CDN) owners. We formulate this joint optimization problem and prove that it can be approximated up to a constant. To the best of our knowledge, this is the first polynomial algorithm to achieve a constant approximation ratio for the joint problem. Our numerical experiments show important performance gains of the proposed algorithm over baseline schemes and existing algorithms.