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With the emergence of pervasive environment, mobile recommender needs to make use of user in-time contextual information to provide personalized recommendation. In this paper, a proactive context-aware news recommender in mobile hybrid P2P network is designed and implemented. We develop a general Analytic Hierarchy Process (AHP) model through empirical studies. We discuss how the relative weight of each AHP criteria can be computed via user assignment and user history. We combine both Contend-based filtering and Collaborative filtering approach to predict user interest using Bayesian Network. The experiments show the system can recommend real time news stories that satisfy the user.