User Application Behavior Sequence Generation

User multi-application behavior simulation has a wide range of applications in many fields. The traditional simulation method calculates the user's behavior law in a probabilistic manner to generate a user behavior simulation sequence. However, the disadvantage of this method is that it can't represent ordinary users on the Internet. This article will study the behavior sequence simulation among multiple applications by giving different machine learning algorithm models. After experiments in this article, we can conclude that the algorithm model adopted in this article can fit the real user behavior in the Internet with a probability of data distribution correlation coefficient of 99%.