Joint Cooperative Content Caching and Recommendation in Mobile Edge-Cloud Networks.

In mobile edge-cloud networks, multiple edge nodes form a mesh network to cooperate with each other. To maximize the benefit of resource-limited edge nodes, the content providers jointly optimize the content caching and recommendation decisions. However, the cooperation between edge nodes complicates both the content caching and recommendation decisions. To solve this problem, in this paper, we propose an efficient joint cooperative content caching and recommendation scheme in edge-cloud networks. Specifically, we formulate the joint cooperative content caching and recommendation problem as an integer-linear programming problem to minimize the average download delay, with controllable user preference distortion tolerance. We propose an efficient heuristic algorithm to solve the formulated problem due to its NP-hardness. We evaluate the performance of the proposed scheme with the MovieLens dataset. The simulation results demonstrate that the proposed scheme can decrease the average download latency by up to 37% and improve average cache hit rate by up to 24%, as compared with state-of-the-art solutions.