Customer Segmentation and Personalized Recommendation Based on Machine Learning

Lingyun Li

doi:10.54097/nwdtkx22

Authors

Lingyun Li

DOI:

https://doi.org/10.54097/nwdtkx22

Keywords:

Customer Segmentation, Personalized Recommendation, Machine Learning, Deep Learning, Recommendation System.

Abstract

Today, with the rapid development of digitalization and networking, enterprises and institutions can collect customer data on an unprecedented scale, including demographic information, consumption behavior records, interaction logs, and social media information. These data provide a rich foundation for in-depth understanding of customer behavior and prediction of future demands. However, in the face of high-dimensional, multi-modal and dynamic data, how to effectively extract information, conduct refined customer segmentation and provide efficient personalized recommendations has become a research hotspot of common concern in both academia and industry. This paper systematically reviews the research progress of machine learning in customer segmentation and personalized recommendation in the past five years (2020-2025), covering aspects such as data feature construction, unsupervised and supervised clustering methods, gradient boosting tree models (LightGBM, CatBoost), deep neural networks (Transformer, GNN), core architectures and hybrid strategies of recommendation systems, model evaluation methods, interpretability and privacy protection. At the same time, it combines actual cases from industries such as retail, e-commerce, finance and healthcare to analyze the application effects and challenges of algorithms in real business, and looks forward to future development trends such as real-time personalization, multi-modal fusion, federated learning and green AI.

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