摘 要
随着互联网技术的迅猛发展，信息过载问题日益凸显，推荐系统作为解决这一问题的有效工具，在电子商务、社交媒体和内容分发等领域发挥着重要作用本研究以机器学习算法在推荐系统中的应用与优化为核心，旨在通过改进算法性能提升用户体验和系统效率研究首先梳理了传统推荐算法的局限性，如协同过滤方法在冷启动和数据稀疏问题上的不足，并结合深度学习、强化学习等前沿技术提出了一种融合多源信息的混合推荐框架该框架通过引入用户行为序列建模和上下文感知机制，显著增强了对动态用户偏好的捕捉能力实验部分采用公开数据集进行验证，结果表明所提方法在准确率、召回率和覆盖率等关键指标上均优于现有主流算法此外，研究还针对模型计算复杂度较高的问题，设计了一种轻量化策略，确保其在资源受限环境下的可部署性总体而言，本研究不仅为推荐系统的性能优化提供了新思路，还为机器学习技术在个性化服务领域的深入应用奠定了理论基础主要贡献在于提出了兼顾效果与效率的推荐算法解决方案，同时为未来相关研究指明了方向
关键词：推荐系统；机器学习；深度学习；混合推荐框架；轻量化策略
Abstract
With the rapid development of Internet technology, the problem of information overload has become increasingly prominent, and recommendation systems have emerged as an effective tool to address this issue, playing a crucial role in areas such as e-commerce, social media, and content distribution. This study focuses on the application and optimization of machine learning algorithms in recommendation systems, aiming to enhance user experience and system efficiency by improving algorithm performance. It begins by reviewing the limitations of traditional recommendation algorithms, such as the inadequacies of collaborative filtering methods in handling cold-start and data sparsity problems, and then proposes a hybrid recommendation fr amework that integrates multi-source information by leveraging advanced techniques like deep learning and reinforcement learning. This fr amework significantly enhances the ability to capture dynamic user preferences through the incorporation of user behavior sequence modeling and context-aware mechanisms. The experimental section validates the proposed method using public datasets, and the results demonstrate superior performance in key metrics such as accuracy, recall, and coverage compared to existing mainstream algorithms. Additionally, addressing the issue of high computational complexity in the model, a lightweight strategy is designed to ensure deployability in resource-constrained environments. Overall, this study not only provides new insights into the performance optimization of recommendation systems but also lays a theoretical foundation for the in-depth application of machine learning technologies in personalized services. Its primary contributions lie in proposing a recommendation algorithm solution that balances effectiveness and efficiency, while also pointing out directions for future related research.
Keywords: Recommendation System；Machine Learning；Deep Learning；Hybrid Recommendation fr amework；Lightweight Strategy
目 录
摘 要 I
Abstract II
一、绪论 1
（一）机器学习与推荐系统的发展背景 1
（二）研究《机器学习算法在推荐系统中的应用与优化》的意义 1
（三）当前研究现状与挑战分析 2
二、推荐系统的机器学习基础 2
（一）推荐系统的核心需求分析 2
（二）常见机器学习算法的适用性探讨 3
（三）数据特征提取与模型选择策略 3
三、机器学习算法的应用实践 4
（一）协同过滤算法的改进与优化 4
（二）深度学习在内容推荐中的应用 4
（三）强化学习对动态推荐的支持 5
四、推荐系统性能优化研究 6
（一）冷启动问题的解决方案探索 6
（二）用户隐私保护的技术实现路径 6
（三）算法效率与推荐质量的平衡策略 7
结 论 7
致 谢 9
参考文献 10
