Research on the Design of a Real-Time E-Commerce Recommendation System Based on Spark in the Context of Big Data

With the rapid development of big data technology, e-commerce platforms require higher levels of real-time performance and accuracy in their recommendation systems. This paper designs and implements a large-scale real-time e-commerce recommendation system based on Spark. The system leverages Spark's distributed computing capabilities and in-memory processing to analyze massive user behavior data in real-time and optimize recommendation algorithms, thereby enhancing recommendation accuracy and response speed. The study first explores the system architecture, analyzing data flow processing and storage strategies, and then optimizes the recommendation algorithm based on this foundation. Experimental results show that this system significantly improves recommendation performance while meeting real-time requirements, providing efficient and accurate personalized recommendation services for e-commerce platforms.