Mobile User Personalized Information Recommendation Research

With the rapid development of 3G and 4G networks, mobile information service can provide a lot of convenience for the mobile users, including weather,real-time news,Geographical, localtion, traffic lines, location information,entertainment information and so on. It is becoming a hot topic in the mobile market.Mobile information service is help mobile users to get latest and useful information to meet the personalized needs for different users.Providing mobile users with the latest, highly mobile personalized information, first you need to get mobile user interest information and based on these interested informations build the user model in early days.This paper introduces on the basis of the research background and research purpose,studies mobile user personalization features, mobile user interest model,mobile user catering personalized recommendation,mobile user movie personalized recommendation.According to the characteristics of the mobile personalized information, put Jaccard similarity coefficient into collaborative filtering and get a prediction results, as a result, get a ideal result in catering personalized recommendation.In the mobile user movie recommendation and study, according to the basic characteristic of the user and user personality information calculate the similarity of users,.the user similarity and time function is considered in the collaborative filtering algorithm, and improve the quality of the recommendations.