Machine Learning Methods in Customer Segmentation and Recommendation Systems

Math & Agricultural and Natural Resource Department, University of Maryland, College Park, Maryland, 20742, United States of America

^* Corresponding author: emilyg9@terpmail.umd.edu

Abstract

As access to all kinds of data becomes more and more available, the need for people to efficiently classify and extract useful data is urgent, especially for businesses. Machine learning has enhanced recommender systems through collaborative filtering, content-based filtering, and hybrid models. Collaborative filtering predicts user preferences based on past interactions but faces cold start and scalability issues. This article shows that content-based filtering uses attributes to recommend items, but relies on metadata quality. Case studies show that Amazon applied collaborative filtering and DBSCAN for fraud detection, improving recommendation accuracy by 12%. Banks use machine learning for segmentation and fraud detection, and PCA improves anomaly detection by 15%. Healthcare applies clustering for patient classification, improving treatment accuracy by 18%. This article points out that current technical challenges include data quality issues, privacy risks, and bias. Poor data quality leads to inaccurate results, while privacy issues (as shown by the Equifax breach) require stronger protection. Future solutions include bias detection, diverse datasets, and encryption techniques to enhance security and reliability.