Collaborative Edge Caching with Personalized Modeling of Content Popularity over Indoor Mobile Social Networks

Mobile social networks allow users to acquire multimedia contents to their mobile devices via wireless communications. To alleviate the network traffic and latency for transmitting contents, user preferences can be predicted and popular contents can be cached at the edge of network. However, for edge caching over the indoor mobile social networks raises challenging issues. The user preference for mobile data is location-dependent in different areas of indoor environment, such that various edge nodes need to maintain its distinctive prediction model instead of using the universal one. The limited computing power for edge nodes over indoor mobile social networks also hinders effective model training on the edge. In this paper, we propose a collaborative edge caching framework that enables personalized modeling for content popularity prediction. Specifically, a non-additive measure based feature selection scheme is proposed to realize efficient yet accurate modeling on resource-constrained edge nodes. A collaborative learning algorithm is designed to reduce the computational overheads over mobile social networks by extracting global knowledge on simplifying model training through feature selection. Extensive simulation validates the effectiveness and efficiency of our proposed framework.