摘 要
随着互联网技术的迅猛发展，用户行为数据呈现爆炸式增长，如何有效分析这些数据并提供个性化的服务成为当前研究的重要课题本研究旨在深入探讨网络应用中的用户行为分析方法及其在个性化推荐系统中的应用通过结合大数据挖掘、机器学习和深度学习等先进技术，提出了一种基于多源行为特征融合的用户画像构建方法，并设计了改进的协同过滤算法以提升推荐系统的性能实验结果表明，所提出的用户行为分析模型能够准确捕捉用户的动态偏好，同时个性化推荐算法在多个评价指标上显著优于传统方法此外，本研究还针对冷启动问题提出了基于内容与协同混合的解决方案，有效缓解了新用户或新物品的推荐难题总体而言，本研究不仅为理解复杂网络环境下的用户行为提供了新的视角，还在实际应用场景中验证了方法的有效性和可扩展性其创新点在于将多源异构数据整合到统一框架中，并通过深度学习技术提取高层次的行为特征，从而显著提升了推荐系统的精准度和用户体验最终，研究成果为推动个性化推荐技术的发展以及优化网络服务提供了重要的理论支持和技术参考
关键词：用户行为分析；个性化推荐系统；多源行为特征融合；协同过滤算法；冷启动问题
Abstract
With the rapid development of Internet technology, user behavior data has experienced explosive growth. How to effectively analyze these data and provide personalized services has become an important research topic. This study aims to explore user behavior analysis methods in network applications and their applications in personalized recommendation systems. By integrating advanced technologies such as big data mining, machine learning, and deep learning, a user profiling construction method based on multi-source behavioral feature fusion is proposed, along with an improved collaborative filtering algorithm designed to enhance the performance of recommendation systems. Experimental results demonstrate that the proposed user behavior analysis model can accurately capture users' dynamic preferences, while the personalized recommendation algorithm significantly outperforms traditional methods in multiple evaluation metrics. Additionally, this study addresses the cold-start problem by proposing a hybrid solution based on content and collaborative filtering, effectively alleviating the challenges of recommending to new users or items. Overall, this research not only provides a new perspective for understanding user behavior in complex network environments but also validates the effectiveness and scalability of the proposed methods in practical application scenarios. Its innovation lies in integrating multi-source heterogeneous data into a unified fr amework and extracting high-level behavioral features through deep learning technology, which substantially improves the accuracy of recommendation systems and enhances user experience. Ultimately, the research findings offer crucial theoretical support and technical references for advancing personalized recommendation technologies and optimizing network services.
Keywords: User Behavior Analysis; Personalized Recommendation System; Multi-Source Behavioral Feature Fusion; Collaborative Filtering Algorithm; Cold Start Problem
目 录
1绪论 1
1.1网络应用与用户行为分析的背景 1
1.2个性化推荐技术的研究意义 1
1.3用户行为分析与推荐技术的现状综述 1
1.4本文研究方法与技术路线 2
2用户行为数据的采集与处理 2
2.1用户行为数据的类型与特征 2
2.2数据采集的技术手段与挑战 3
2.3数据清洗与预处理方法 3
2.4行为数据的质量评估标准 4
2.5数据隐私保护与伦理问题 4
3用户行为模式的建模与分析 5
3.1用户行为模式的定义与分类 5
3.2基于统计学的行为建模方法 5
3.3基于机器学习的行为分析框架 6
3.4动态行为模式的识别与预测 6
3.5行为分析中的偏差与校正策略 7
4个性化推荐算法的设计与优化 7
4.1推荐系统的常见算法类型 7
4.2基于内容的个性化推荐模型 8
4.3协同过滤在推荐系统中的应用 8
4.4深度学习驱动的推荐算法改进 9
4.5推荐效果的评估与优化策略 9
结论 10
参考文献 11
致 谢 12
