User Behavior Prediction Based on DCGAN: The Case of Sina Weibo

Abstract

E-commerce marketing forces are taking advantage of microblogs to deliver their advertisements to promote product information. The success of product information diffusion in microblog depends greatly on user behaviors -- browsing, commenting and reposting. In this paper, we divide user behaviors of Sina Weibo into four types corresponding to four different colors, and propose a method to predict user behavior based on DCGAN (Deep Convolutional Generative Adversarial Nets). By analyzing a real Sina Weibo dataset, the experimental results show that the prediction accuracy of the four types of user behaviors reaches more than 80%, which proves that our method is feasible and effective, and also can help companies succeed in their product advertisements.