The Research Of The Usage Patterns And Optimization Of The Smartphone Application For User Experience

Follow the development of Mobile Internet,big data research becomes popular to Mobile Internet.’Mobile Internet’ and ’Big Data’have become the hottest topics in the internet area.In those topics,mobile application has the most direct relationship with users.But most of the users find it difficult choosing appropriate applications to use.Under this circumstances,it becomes more and more important to find the fittest application and enhancing their use experiences in massive data.Based on those information,this paper will start with the traffic usage,in combination with user preferences and then recommend the applications which will consume less traffic and are more popular.First,this paper studies how to build the Hadoop platform,then implements the platform based on Ambari..With the platform,we could do some classification and then process and analyze the dataset.Second,based on users’ preferences and APP pattern we built the recommend model.According to the model,we get users ’preferences,and then recommend them with those applications which will consume less traffic and are more popular.Finally we aim to enhance users’experience.One more thing,we have to study the using period of either the user or the APP.In the end,we verify the model with the mobile internet dataset,the result shows that under the condition that we satisfy users ’preferences,the applications that this platform recommends will consume less traffic or are more popular.Finally their experiences get enhanced.