Abstract: In dealing with the needs of real-time personalized recommendation，personalized recommendation method for E-Commerce system is studied through deep mining of customer’s browse record based on information flow representation，generation and analysis.In order to manage information flow，this paper proposes a personalized recommendation algorithm，which consists of four phases：information flow definition，information flow evaluation，knowledge matching and website dynamic organization.A model including client，server and storage system is designed based on J2EE.To demonstrate the practical usefulness of this methodology，a real-life case is illustrated，which shows recommendation accuracy increases according to the increase of training number，and superior to association rule.

Key words: personalized recommendation, information flow, algorithm design

摘要： 针对企业对实时个性化推荐的需求，基于信息流的表达、生成和分析对顾客访问记录进行了深层次挖掘，研究了电子商务系统实时个性化推荐策略。提出了包含信息流定义、信息流评价、知识匹配、网站动态组织等算法在内的个性化推荐算法。基于J2EE技术完成了包含客户端、服务器端和存储系统在内的实时动态个性化推荐系统的结构设计。实例表明，随着训练次数的增加推荐准确度呈升高的趋势，并优于关联规则法。

关键词: 个性化推荐, 信息流, 算法设计