摘 要
随着信息技术的迅猛发展和互联网用户规模的持续扩大，个性化推荐系统已成为解决信息过载问题的重要手段之一，其核心目标是通过分析用户行为和偏好，提供精准、个性化的服务。本研究聚焦于机器学习技术在个性化推荐系统中的应用，旨在探索如何利用先进的算法框架提升推荐系统的性能与用户体验。研究首先梳理了当前主流的机器学习方法，包括协同过滤、深度学习和强化学习等，并结合实际应用场景分析了各类算法的优势与局限性。在此基础上，提出了一种基于深度神经网络的混合推荐模型，该模型通过整合用户隐式反馈与显式特征，有效提升了推荐结果的相关性和多样性。实验部分采用公开数据集进行验证，结果显示所提出的模型在准确率、召回率和覆盖率等关键指标上均优于传统方法。
关键词：个性化推荐系统 机器学习 深度神经网络
Abstract
With the rapid development of information technology and the continuous expansion of the scale of Internet users, personalized recommendation system has become one of the important means to solve the problem of information overload. Its core goal is to provide accurate and personalized services by analyzing user behaviors and preferences. This study focuses on the application of machine learning technology in personalized recommendation systems, and aims to explore how to use the advanced algorithm fr amework to improve the performance and user experience of recommendation systems. The research first sorts out the current mainstream machine learning methods, including collaborative filtering, deep learning and reinforcement learning, and analyzes the advantages and limitations of various algorithms in combination with practical application scenarios. Then, a hybrid recommendation model based on deep neural network is proposed, which effectively improves the relevance and diversity of recommendation results by integrating user implicit feedback and explicit features. The experimental part was verified using publicly available datasets, and the results showed that the proposed model outperformed the traditional methods in key indicators such as accuracy, recall and coverage.
Keyword:Personalized Recommendation System Machine Learning Deep Neural Network
目 录
1绪论 1
1.1个性化推荐系统的研究背景与意义 1
1.2机器学习在推荐系统中的应用现状 1
1.3本文研究方法与技术路线 2
2机器学习算法在推荐系统中的基础理论 2
2.1推荐系统的常见类型与特点 2
2.2机器学习算法的基本原理与分类 3
2.3基于内容的推荐算法分析 3
2.4协同过滤推荐算法的改进研究 4
2.5深度学习在推荐系统中的初步应用 4
3个性化推荐系统的关键技术研究 4
3.1用户行为数据的采集与预处理 5
3.2特征提取与表示学习方法 5
3.3数据稀疏性问题的解决策略 5
3.4冷启动问题的优化方案探讨 6
3.5推荐结果的多样性与准确性权衡 6
4实验设计与案例分析 7
4.1实验环境与数据集选择 7
4.2不同算法的性能对比分析 7
4.3用户反馈机制对推荐效果的影响 8
4.4实际应用场景中的个性化推荐实现 8
4.5系统性能评估与改进建议 9
结论 9
参考文献 11
致谢 12
