Design of e-commerce user behavior analysis and personalized recommendation system based on Spark
基于Spark的电商用户行为分析与个性化推荐系统设计

摘要

本论文探讨了基于Spark的电商用户行为分析与个性化推荐系统的设计与实现，重点分析了如何利用大数据技术高效处理海量用户数据，并通过分析用户行为数据进行精准的个性化推荐。论文介绍了电商平台在大数据环境下的需求和挑战，阐述了Spark在数据处理中的优势，并分析了基于Spark的用户行为分析方法。结合机器学习算法，设计了适用于电商平台的个性化推荐系统，探讨了推荐算法的优化策略和实际应用效果。论文总结了基于Spark的电商用户行为分析与个性化推荐系统的实现过程与应用前景。

Abstract

This thesis explores the design and implementation of an e-commerce user behavior analysis and personalized recommendation system based on Spark, with a focus on analyzing how to efficiently process massive user data using big data technologies and achieve accurate personalized recommendations through the analysis of user behavior data. The thesis introduces the demands and challenges of e-commerce platforms in the big data environment, expounds on the advantages of Spark in data processing, and analyzes Spark-based user behavior analysis methods. Combined with machine learning algorithms, a personalized recommendation system suitable for e-commerce platforms is designed, and the optimization strategies of recommendation algorithms and their practical application effects are discussed. Finally, the thesis summarizes the implementation process and application prospects of the Spark-based e-commerce user behavior analysis and personalized recommendation system.