Enhancing E-Commerce Recommendation Systems Through Vector Database Management

This paper explores how vector database management systems (VDBMSs) can enhance the performance, scalability, and precision of modern e-commerce recommendation systems. By integrating vector databases with collaborative filtering, content-based, and hybrid recommendation models, this research demonstrates how high-dimensional data can be stored and retrieved efficiently to generate real-time, personalized suggestions. The study also discusses technical methods such as k-nearest neighbor (k-NN), approximate nearest neighbor (ANN), and retriever–ranker architectures that optimize similarity search and recommendation accuracy. Implemented in Python, the project highlights the growing importance of vector data management in artificial intelligence, data retrieval, and machine learning applications.