摘要
根据浏览历史对用户进行有效聚类,建立基于用户聚类的用户浏览行为预测模型是Web环境下实现个性化服务的关键。该文对系统用户进行聚类,产生相似用户群,根据每个相似用户群的浏览特征,建立基于相似用户群的类Markov链用户浏览行为预测模型,实验验证了该模型的有效性。
In Web environment, clustering Web users based on the browsing behaviors and building user browsing sequences prediction model based on user cluster are keys to achieve the personalized services. This paper produces similar user groups through clustering Web users. According to browsing features of each similar user group, the classified Markov chains based on the different similar user groups are built. Experimental result shows the efficiency of the model.
出处
《计算机工程》
CAS
CSCD
北大核心
2008年第22期32-33,36,共3页
Computer Engineering
基金
天津市高等学校科技发展基金资助项目(20061015)
