摘 要
随着信息技术的迅猛发展，个性化推荐系统在电子商务、社交网络等领域发挥着日益重要的作用。基于协同过滤的个性化推荐系统旨在通过分析用户行为数据挖掘潜在兴趣偏好，为用户提供精准化服务。本研究聚焦于改进传统协同过滤算法存在的稀疏性与可扩展性问题，提出一种融合多源异构数据的混合协同过滤推荐模型。该模型不仅整合了用户评分、浏览历史等显式反馈信息，还引入了社交关系、地理位置等隐式反馈特征，构建了更全面的用户画像。采用矩阵分解技术对大规模稀疏矩阵进行降维处理，并结合深度神经网络提取高阶特征表示，有效提升了推荐效果。实验结果表明，在多个公开数据集上，所提方法相较于经典协同过滤算法及现有改进方案，在准确率、召回率等评价指标方面均有显著提升。此外，针对冷启动问题，创新性地提出了基于图卷积网络的预训练机制，能够快速生成新用户的初始偏好向量，进一步增强了系统的鲁棒性和实用性。研究表明，融合多源数据与深度学习技术的协同过滤推荐系统具有广阔的应用前景，为解决推荐系统面临的挑战提供了新的思路和方法。
关键词：个性化推荐系统；协同过滤算法；多源异构数据；矩阵分解；深度神经网络
Abstract
With the rapid development of information technology, personalized recommendation systems have been playing an increasingly important role in e-commerce, social networks, and other domains. Personalized recommendation systems based on collaborative filtering aim to analyze user behavior data to uncover latent interest preferences, thereby providing precise services to users. This study focuses on addressing the sparsity and scalability issues inherent in traditional collaborative filtering algorithms by proposing a hybrid collaborative filtering recommendation model that integrates multi-source heterogeneous data. The model not only consolidates explicit feedback information such as user ratings and browsing history but also incorporates implicit feedback features like social relationships and geographical locations, thus constructing a more comprehensive user profile. Matrix factorization techniques are employed to reduce the dimensionality of large-scale sparse matrices, while deep neural networks are utilized to extract higher-order feature representations, effectively enhancing recommendation performance. Experimental results demonstrate that, on multiple public datasets, the proposed method significantly outperforms classical collaborative filtering algorithms and existing improved solutions in evaluation metrics such as accuracy and recall. Additionally, an innovative pre-training mechanism based on graph convolutional networks is introduced to address the cold start problem, enabling the rapid generation of initial preference vectors for new users, thereby further enhancing the robustness and practicality of the system. The research indicates that collaborative filtering recommendation systems that integrate multi-source data and deep learning technologies hold broad application prospects and provide new approaches and methods for tackling challenges faced by recommendation systems.
Keywords：Personalized Recommendation System；Collaborative Filtering Algorithm；Multi-source Heterogeneous Data；Matrix Factorization；Deep Neural Network
目 录
摘 要 I
Abstract II
引 言 1
第一章 协同过滤技术综述 2
1.1 协同过滤基本原理 2
1.2 用户 2
1.3 传统协同过滤方法 3
1.4 协同过滤面临的挑战 3
第二章 数据预处理与特征提取 5
2.1 数据收集与清洗 5
2.2 用户行为特征分析 5
2.3 项目属性特征提取 6
2.4 数据降维与归一化 6
第三章 协同过滤算法优化 8
3.1 算法改进思路 8
3.2 基于邻域的协同过滤 8
3.3 基于模型的协同过滤 9
3.4 混合协同过滤策略 9
第四章 推荐系统性能评估 11
4.1 评价指标体系 11
4.2 实验设计与数据集 11
4.3 精度与召回率分析 12
4.4 用户满意度调查 12
结 论 14
参考文献 15
致 谢 16
