Wireless service prediction algorithm in mobile social environment

First, according to characteristics of mobile social environment, by using optimization models based on similarity degree and interaction degree respectively, the optimal correlated users can be selected for analyzing two main factors of a target user's behaviors (i.e. long-term habits and short-term influences); furthermore, an adaptive update strategy based on fuzzy theory is proposed to describe the importance of two factors in real time and quantitative manners. Second, an improved Apriori theory is introduced to predict user service behaviors accurately; particularly, a new update mechanism for Apriori sample database is built to effectively integrate the samples of optimal correlated users. Finally, simulation results verify the effectiveness of proposed algorithm.