摘 要
随着信息技术的迅猛发展，大数据技术在电子商务领域的应用日益广泛，其中个性化推荐系统成为提升用户体验和商业价值的核心工具。本研究旨在探讨大数据技术如何优化电子商务个性化推荐系统的性能与效果，通过整合海量用户行为数据、商品信息及外部环境变量，构建更加精准和高效的推荐模型。研究采用混合方法论，结合定量分析与定性评估，利用机器学习算法对大规模数据集进行深度挖掘，并引入深度神经网络以捕捉复杂的用户偏好模式。实验结果表明，基于大数据驱动的推荐系统能够显著提高预测准确率和用户满意度，同时有效缓解冷启动问题和数据稀疏性挑战。本研究的创新点在于提出了一种融合多源异构数据的推荐框架，该框架不仅增强了系统的泛化能力，还实现了动态实时更新，从而适应快速变化的市场环境。此外，研究进一步验证了情境感知技术在个性化推荐中的重要性，为未来相关领域的研究提供了新的思路和方向。总体而言，本研究揭示了大数据技术在电子商务个性化推荐中的关键作用，为行业实践和技术发展奠定了坚实基础。
关键词：大数据技术；个性化推荐系统；机器学习算法；多源异构数据；情境感知技术
Abstract
With the rapid advancement of information technology, the application of big data technology in the e-commerce domain has become increasingly extensive, with personalized recommendation systems emerging as a core tool for enhancing user experience and commercial value. This study aims to investigate how big data technology can optimize the performance and effectiveness of personalized recommendation systems in e-commerce by integrating massive amounts of user behavior data, product information, and external environmental variables to construct more precise and efficient recommendation models. A mixed-methods approach is employed, combining quantitative analysis with qualitative evaluation, utilizing machine learning algorithms for in-depth mining of large-scale datasets and introducing deep neural networks to capture complex user preference patterns. The experimental results demonstrate that a big data-driven recommendation system can significantly improve prediction accuracy and user satisfaction while effectively alleviating issues such as cold start problems and data sparsity challenges. The innovation of this study lies in proposing a recommendation fr amework that fuses multi-source heterogeneous data, which not only enhances the system's generalization capability but also enables dynamic real-time updates, thereby adapting to rapidly changing market environments. Furthermore, the study validates the importance of context-aware technology in personalized recommendations, providing new insights and directions for future research in related fields. Overall, this study elucidates the critical role of big data technology in personalized recommendations within e-commerce, laying a solid foundation for both industry practice and technological development.
Keywords: Big Data Technology; Personalized Recommendation System; Machine Learning Algorithm; Multi-Source Heterogeneous Data; Context-Aware Technology
目 录
一、绪论 1
（一）研究背景与意义 1
（二）国内外研究现状分析 1
（三）本文研究方法概述 2
二、大数据技术在个性化推荐中的基础理论 2
（一）大数据技术的核心概念 2
（二）个性化推荐系统的原理与框架 3
（三）数据采集与预处理的关键步骤 3
（四）推荐算法的分类与特点 4
（五）大数据与推荐系统的技术融合 4
三、大数据驱动的个性化推荐算法优化 5
（一）基于协同过滤的推荐算法改进 5
（二）深度学习在推荐系统中的应用 6
（三）实时推荐算法的设计与实现 6
（四）用户行为数据分析方法 7
（五）算法性能评估指标体系 7
四、电子商务场景下的个性化推荐实践 8
（一）电子商务平台的数据特征分析 8
（二）用户画像构建与应用场景 8
（三）跨平台数据整合与利用 9
（四）推荐效果对业务的影响评估 9
（五）隐私保护与数据安全挑战 10
结论 10
参考文献 12
致 谢 13
