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Edge-caching is recognized as an efficient technique for future wirelesscellular networks to improve network capacity and user-perceived quality ofexperience. Due to the random content requests and the limited cache memory,designing an efficient caching policy is a challenge. To enhance theperformance of caching systems, an accurate content request predictionalgorithm is essential. Here, we introduce a flexible model, a Poissonregressor based on a Gaussian process, for the content request distribution instationary environments. Our proposed model can incorporate the contentfeatures as side information for prediction enhancement. In order to learn themodel parameters, which yield the Poisson rates or alternatively contentpopularities, we invoke the Bayesian approach which is very robust againstover-fitting.  However, the posterior distribution in the Bayes formula is analyticallyintractable to compute. To tackle this issue, we apply a Monte Carlo MarkovChain (MCMC) method to approximate the posterior distribution. Two types ofpredictive distributions are formulated for the requests of existing contentsand for the requests of a newly-added content. Finally, simulation results areprovided to confirm the accuracy of the developed content popularity learningapproach.

A Feature-Based Bayesian Method for Content Popularity Prediction in Edge-Caching Networks