Realizing Adaptive Web Cites Based on Correlative Page Set
基于闭相关页面集实现网站自适应

Adaptive web cites can automatically and flexibly improve their organization to fit users' requirements and application environments that are always changing continually.According to concept of frequent-close-item-set based on graph,this paper provides method of on-line dynamic recommendation based on close correlative page set mined from websites' logs of a period.This method improves problems of sparsity and expansibility of traditional collaborative rec- ommendation and enhances quality of service without increasing servers' load.Finally this paper analyzes two methods of measuring performance of recommendation system and evaluates the system.