Bayesian Poisson Factorization With Side Information for User Interest Prediction in Hierarchical Edge-Caching Systems

Edge-caching is an effective solution to cope with the unprecedented data traffic growth by storing contents in the vicinity of end-users. In this paper, we formulate a hierarchical caching policy where the end-users and cellular base station (BS) are equipped with limited cache capacity with the objective of minimizing the total data traffic load in the network. The caching policy is a nonlinear combinatorial programming problem and difficult to solve. To tackle the issue, we design a heuristic algorithm as an approximate solution which can be solved efficiently. Moreover, to proactively serve the users, it is of high importance to extract useful information from data requests and predict user interest about contents. In practice, the data often contain
implicit feedback
from users which is quite noisy and complicates the reliable prediction of user interest. In this regard, we introduce a Bayesian Poisson matrix factorization model which utilizes the available side information about contents to effectively filter out the noise in the data and provide accurate prediction. Subsequently, we design an efficient Markov chain Monte Carlo (MCMC) method to perform the posterior approximation. Finally, a real-world dataset is applied to the proposed proactive caching-prediction scheme and our results show significant improvement over several commonly-used methods. For example, when the BS and the users have caches with storage of 25% and 10% of the total contents size respectively, our approach yields around 8% improvement with respect to the state-of-the-art approach in terms of caching performance.