Mobile Phone User Behavior Prediction Base on Multivariable Linear Regression Model

Abstract

With the development of the Internet industry and the smart phone industry, the amount of mobile application software is increasing and the variety is becoming more and more abundant. The ensuing question is how mobile users can choose the applications they are interested in among many mobile applications. Therefore, mobile application recommendation service for mobile phone users came into place. This paper takes mobile phone users as the starting point, uses multiple linear regression models, uses the commonly used least mean square to evaluate the error, and uses the batch gradient descent method to optimize the training analysis error. Through experiments on the usage data of a large number of mobile phone users, the relationship between gender, age, mobile phone model, mobile phone usage location and app category of the mobile phone user is analyzed, and the mobile phone user app category is predicted to personalized recommend apps for the mobile phone user.