Optimizing Network Performance Through Joint Caching and Recommendation Policy for Continuous User Request Behavior

Edge caching is a widely adopted technique for improving network performance and user experience. To optimize its benefits, researchers are exploring the use of recommendation systems, which can leverage advanced algorithms to determine which content should be cached at the edge, leading to lower latency, reduced bandwidth usage, and ultimately better network and service management. The majority of existing works, on the other hand, are based on independently and identically distributed request patterns. In this work, the problem of joint caching and recommendation policy for long-term user request behavior was studied, which is more realistic. Specifically, the continuous request behavior of users was regarded as being relevant and was modeled as an Absorbing Markov Chain. The goal was to minimize the expected content delivery cost over multiple viewing sessions while meeting the requirements for expected value on quality of recommendation. The optimization problem was formulated and transformed into a mixed-integer programming (MIP) problem, which provides an optimal solution (JCRP). Meanwhile, a heuristic algorithm with low computational complexity that is more friendly to network resources has been proposed (LvJCR). Simulation results show that the proposed algorithms have advantages in cost minimization over long sessions.