Temporal-Spatial Collaborative Mobile Edge Caching With User Satisfaction Awareness

For mobile proactive edge caching problems, the hit action cannot always reflect the efficiency of the cache strategy unilaterally. Thus, the satisfaction information of users should be used to improve the intelligent cache strategy. Motivated by this, this paper focuses a caching scheme that jointly considers the cache evaluation both from the perspectives of the hit action and satisfaction of users (Algorithm 3). Accordingly, we first propose a requesting behavior prediction model (Algorithm 1) to handle the multiple content features. Then, a requesting behavior factor learning algorithm (Algorithm 2) based on the online gradient descent (OGD) with regulations is proposed to obtain the estimation of the parameters of the model in Algorithm 1. Further, the probabilistic tensor factorization learning (PTFL) model with collaborative filtering and Markov Chain Monte Carlo (MCMC) procedure are introduced for the learning process. Finally, the proposed the caching Algorithm 3 based on the hit action prediction and the collaborative temporal-spatial satisfaction learning scheme can be performed. Moreover, we theoretically combine the upper limit performance of user request prediction errors with the lower limit of user satisfaction of edge cache. The experimental results verify the effectiveness of the proposed algorithm.