User-centric optimization of caching and recommendations in edge cache networks

In platforms such as Youtube, Netflix, and Spotify, recommendations influence a large share of content consumption. In this context, user experience depends on both the quality of the recommendations (QoR) and the quality of service (QoS) of the delivered content. Nevertheless, 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 both caching and recommendations on top of a generic network of caches, towards maximizing the quality of experience (QoE). This is in line with the recent trend for large content providers to also act as 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.