Research And Implementation Of Mobile User Preference Predictionin Based On Link Prediction Model

In recent years, with the quick development of mobile terminal both in hardware and software, there is an increasing requirement for mobile communication networks. Mobile terminal is becoming more popular for its convenience as well as the increasing transmission and load-bearing capability. What’s more, twitter, social networks and online shopping is prevailing during this period, and our life is becoming awesome for various of mobile communication network resources. Meanwhile, it brings us a deteriorating mobile information overloading problem as well. User preference prediction is one of the most important ways to alleviate information overloading problem, so it becomes the primary way when people handle this problem in the field of mobile communication networks. Compared with traditional Internet, there are less network recourses in mobile communication networks and the I/O ability of the devices which are used to receiving mobile information is also limited. So it is becoming crucial to find out what the mobile users really want in the vast recourse ocean so as to enhance the quality of personalized mobile service. And all above raise a higher requirement for the research work of mobile user preference prediction.In order to obtain more accurate mobile user preferences with the characteristics of mobile communication networks and the increasing personalized service requirement. In this paper we study and summarize mobile user preference through trust degree, link prediction and time decay. Firstly, we propose a mobile user trust calculation method through analyzing user communication behavior and put it into mobile user preference prediction. And we ascertain the mobile applications which are to be predicted before user preference prediction according to the nearest neighbors of the active users. Then we imitate the decay tendency with some decay functions according the decay feature of user preference and propose an improved user preference prediction method. Experimental results show that the improved method can obtain more accurate user preferences compared with traditional collaborative filtering as well as mitigating data sparsity problem and extensibility problem.