Prediction of User Consumption Behavior Data Based on the Combined Model of TF-IDF and Logistic Regression

Abstract

In the era of the rapid development of computers and the Internet, e-commerce has become a part of the economy of many countries. Therefore, how to use historical data of user consumption behavior to predict user shopping intentions accurately and subsequent personalized recommendations has turned into research hotspots in the field of e-commerce. This paper conducts a basic analysis of JD's e-commerce data based on machine learning. Specifically, this article constructs user-product matrix and product and user clustering by means of text processing and clustering, as well as implements a logistic regression classifier to predict the user's purchase intention of products in a certain target category in the next 5 days. Based on the JD competition data set, this article has a prediction accuracy rate of 98%. This can help e-commerce companies make better decisions.