Joint Content Valuations and Proactive Caching for Content Distribution Networks

Due to the advances in machine learning techniques, recommender systems nowadays are capable of learning and influencing the users' decisions. Hence, recommendations became an important facility to reduce the cost (or increase the profit) of the operators of the demand networks. In this paper we formulate and study the problem of dynamically optimizing the demand shaping, through content recommendation, and proactive caching. The formulated problem suffers from the curse of dimensionality, so we devise an approximate algorithm optimizing only over a short look-ahead window. The approximate problem is not convex, as such we utilize non-convex optimization techniques to tackle the problem. To verify the efficiency of our proposed solution, we establish a lower bound on the minimum achievable cost and contrast it with our solution.