Research on classified prediction algorithm of platform customer value based on "tree" concept
Year: 2024
Type: article
Abstract: The concept of "trees," representing decision trees, random forests, and XGBoost algorithms, has gained increasing attention in the field of classification prediction in recent years. These tree-based machine learning algorithms have been widely applied in the e-commerce sector. This paper discusses the decision tree, random forest, and XGBoost algorithms individually, and extracts useful classification rules from large volumes of online customer data to provide intelligent decision support for computer network customer management. The study finds that algorithms related to the "tree" concept can integrate a customer’s current value (such as purchasing behavior) with potential value (such as customer interest inferred from online reviews) through detailed classification criteria, thereby constructing a customer value measurement model. The evolution from decision trees to random forest and XGBoost algorithms has effectively promoted research on customer value hierarchy prediction, improving the efficiency of data mining in computer networks.
Authors: Zening Cheng, Shuangbin Tao, Meiling Ge, Weiwei Xing, Tianyu Li
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4402420782