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为提高网络信息激增中个性化信息推荐的有效性和智能性,将关联规则技术和Multi-Agent技术应用到个性化信息推荐中,设计一个通过对用户日志挖掘以产生个性化信息推荐的系统PIRS。该系统包含6个不同层次具有独立功能而又相互关联的Agent任务模块,引入多个Agent收集和分析用户信息来学习用户的兴趣和行为,体现个性化信息推荐的智能性;利用PIRAgent在用户日志中进行挖掘时,采用的关联规则挖掘方法是基于位对象技术和改进的FP-Tree构造方法,提高系统推荐效率。
In order to improve the effectiveness and intelligence of personalized information recommendation in the proliferation of network information, the association rule technology and Multi-Agent technology are applied to the personalized information recommendation, and a system PIRS which is generated by user log mining to generate personalized information recommendation . The system consists of 6 different levels of independent and interrelated Agent task module, introduces a number of Agent to collect and analyze user information to learn the user’s interest and behavior, reflect the intelligence of personalized information recommendation; the use of PIRAgent in the user log In mining, the association rule mining method is based on the object technology and the improved FP-Tree construction method to improve the system efficiency.