基于动态用户兴趣建模，提升推荐准确率与满意度
Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model
- 构建动态用户兴趣模型，实时捕捉行为数据变化
- 融合多算法推荐，显著提升准确率与用户满意度
- 适合做个性化推荐系统优化的研究与应用
随着互联网快速发展和信息爆炸，为用户提供精准个性化推荐已成为重要研究课题。本文设计并分析了一种基于动态用户兴趣模型的个性化推荐系统。该系统通过采集用户行为数据，构建动态用户兴趣模型，并融合多种推荐算法，为用户提供个性化内容。实验结果表明，该系统显著提升了推荐准确率和用户满意度。论文详细讨论了系统的架构设计、算法实现及实验结果，并探讨了未来研究方向。
原文摘要 · Abstract (English)
With the rapid development of the internet and the explosion of information, providing users with accurate personalized recommendations has become an important research topic. This paper designs and analyzes a personalized recommendation system based on a dynamic user interest model. The system captures user behavior data, constructs a dynamic user interest model, and combines multiple recommendation algorithms to provide personalized content to users. The research results show that this system significantly improves recommendation accuracy and user satisfaction. This paper discusses the system's architecture design, algorithm implementation, and experimental results in detail and explores future research directions.
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