基于大数据的智能推荐系统优化
随着信息技术的迅猛发展，数据量呈爆炸式增长，如何从海量数据中挖掘用户需求并提供精准推荐成为研究热点。本研究聚焦于基于大数据的智能推荐系统优化，旨在通过融合多源异构数据提升推荐系统的准确性和时效性。针对传统推荐算法在处理大规模稀疏数据时存在的局限性，提出一种结合深度学习与图神经网络的混合推荐模型。该模型首先利用深度自编码器对用户行为数据进行特征提取，构建低维稠密表示；然后引入图神经网络捕捉用户-项目交互关系中的复杂结构信息，实现更精准的关联预测。实验结果表明，在多个公开数据集上，所提方法相较于传统协同过滤和矩阵分解算法，在推荐精度方面有显著提升，特别是在冷启动场景下表现优异。
关键词：大数据智能推荐 深度学习 特征提取
Abstract
With the rapid development of information technology, data volumes have experienced explosive growth, making it a research hotspot to mine user needs and provide precise recommendations from massive datasets. This study focuses on optimizing intelligent recommendation systems based on big data, aiming to enhance the accuracy and timeliness of recommendation systems by integrating multi-source heterogeneous data. Addressing the limitations of traditional recommendation algorithms in handling large-scale sparse data, this research proposes a hybrid recommendation model that combines deep learning with graph neural networks. The model first employs deep autoencoders for feature extraction from user behavior data to construct low-dimensional dense representations; it then incorporates graph neural networks to capture complex structural information in user-item interactions, achieving more accurate association predictions. Experimental results demonstrate that, on multiple public datasets, the proposed method significantly outperforms traditional collaborative filtering and matrix factorization algorithms in terms of recommendation accuracy (e.g., root mean square error, mean absolute error), particularly excelling in cold-start scenarios. Furthermore, by introducing an attention mechanism, the model can explain key factors behind recommendation results, enhancing system interpretability. This study not only provides new technical pathways for intelligent recommendation systems but also offers effective solutions to personalized service challenges in big data environments, holding significant theoretical and practical value.
Keyword:Big Data Intelligent Recommendation deep learning Feature Extraction
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 2
2大数据特征分析 2
2.1数据规模与多样性 2
2.2数据处理技术需求 3
2.3数据质量评估方法 3
3推荐算法优化策略 4
3.1常见推荐算法评述 4
3.2算法性能改进方向 5
3.3个性化推荐实现 5
4系统架构设计优化 6
4.1架构模式选择依据 6
4.2关键技术组件集成 7
4.3系统性能优化措施 7
结论 8
参考文献 10
致谢 11
