Pre-fetching Webpages on Mobile Social Network: User-Aware Dynamic Markov Chain

Different users, different servicing is an important commercial strategy. As VIP users are the main source of revenue, how to provide precise and personalized services for them becomes a hot spot for Mobile Social Network(MSN) providers. Effective pre-fetching of web-pages can improve Quality of Experience (QoE) for MSN users by reducing latency perceived from end-to-end. In this paper, we propose a novel user-aware dynamic Markov chain model to provide personalized pre-fetching for VIP users while guaranteeing the common pre-fetching for ordinary users. It can avoid the weak points generated by applying the former pre-fetching mechanisms to MSN: non-user awareness, low accuracy, high complexity, and repetitive training. Based on real click-stream data of wap.renren.com collected from a main Mobile Telecom Carrier in Chongqing province of China, we evaluate the model.