本文分析了信息检索中的相关性反馈技术，对用户兴趣的反馈评价机制进行了研究。采用Agent技术，给出了一个智能Agent的相关度反馈模型；并对此模型中的用户兴趣的反馈评价机制进行了实验，通过改进的ID3算法，学习用户兴趣、更新用户兴趣模型来实现个性化的信息检索。实验证明，这种反馈评价机制是有效的，系统的查全率与查准率都有了明显的提高，能够更好地满足用户的个性化需求。

This paper analyzes the information retrieval techniques for relevance feedback, and the user interest feedback evaluation mechanisms are  studied. Using the agent technology, a relevance feedback model based on intelligent agents is given; and the user interest feedback evaluation mechanism of this model is experimented. Through the improved ID3 algorithms to learn the user interest and update the  user interest model,the personalized information retrieval is achieved. Experiments show that this feedback mechanism is an effective evaluation system,and the recall rate and the precision rate have obvious improvement to better meet the user's individual requirements.