大数据时代下个性化推荐的优化与应用研究

Research on Optimization and Application of Personalized Recommendation in Big Data Era

随着信息技术的快速发展和海量数据的生成,个性化推荐在电子商务、社交网络、搜索引擎等领域得到了广泛应用,成为提升用户体验和增长业务的关键因素。个性化推荐通过整合多种技术,如大数据分析、机器学习和人工智能等,为用户提供了定制化内容和服务。个性化推荐的优化策略涉及引入时间衰减因子、融合多维度特征、采用深度学习模型等多方面。本文通过实际案例分析,探讨了个性化推荐在不同领域的应用效果,旨在为相关研究和实践提供参考。

With the rapid development of information technology and the generation of massive data,personalized recommendation has been widely used in e-commerce,social networks,search engines and other fields,and has become a key factor in improving user experience and growing business.Personalized recommendation provides users with customized content and services by integrating multiple technologies,such as big data analysis,machine learning,and artificial intelligence.The optimization strategy of personalized recommendation involves various aspects such as introducing time decay factor,fusing multi-dimensional features,and adopting deep learning models.This paper explores the application effect of personalized recommendation in different fields through actual case studies,aiming to provide reference for related research and practice.