基于大数据分析的智能推荐系统的设计与实现

Design and Implementation of An Intelligent Recommendation System Based on Big Data Analysis

摘要:

传统推荐技术在海量数据处理、算法复杂性以及用户隐私保护等方面存在明显局限。大数据分析技术为推荐系统的发展提供了新的动力,其能通过对多源异构数据的高效处理与深度挖掘,实现用户行为精准建模与推荐结果动态优化。该系统采用六层分布式微服务架构,融合协同过滤与深度学习算法,实现了从数据采集到算法推荐的完整技术体系。测试结果表明,智能推荐系统在准确率、响应效率等核心指标方面展现出显著优势,为推荐领域提供了一种高效、可行的技术解决方案。

Abstract:

Traditional recommendation technologies exhibit significant limitations in handling massive datasets, algorithmic complexity, and safeguarding user privacy. Big data analytics has provided fresh impetus for recommendation system development, enabling precise user behaviour modelling and dynamic optimisation of recommendation outcomes through efficient processing and deep mining of multi-source heterogeneous data. This system adopts a six-layer distributed microservices architecture, integrating collaborative filtering with deep learning algorithms to establish a comprehensive technical framework spanning data acquisition to algorithmic recommendation. Test results demonstrate that the intelligent recommendation system exhibits significant advantages in core metrics such as accuracy and response efficiency, offering an efficient and viable technical solution for the recommendation domain.