Research on a Prediction Model of Online Shopping Behavior Based on Deep Forest Algorithm

摘要

E-commerce platforms such as Taobao can collect massive users 'shopping behavior data, which makes it possible to grasp users' shopping preferences. However, the current research methods of user operation behavior prediction usually only analyze a certain type of user's operation behavior, which cannot fully reflect the overall characteristics of user behavior. Based on the shopping behavior data of Alibaba's e-commerce platform, this article mines user characteristics, product characteristics, product category characteristics, user-product characteristics, and user-product category characteristics from a large amount of online shopping behavior data. The online purchasing behavior prediction model has achieved better results than other models. Experimental results show that the model improves the accuracy of prediction while reducing the time overhead.