CAJ | 학술논문

传统 Markov 链模型在用户浏览行为预测方面体现出较好的性能，但不能很好的体现出用户的兴趣度和所推荐的页面的重要性，因此本文提出类时齐 Markov 模型。该模型给不同的类别用户单独创建时齐 Markov 模型，并用时齐 Markov 模型的平稳分布表征用户的访问兴趣和页面的重要程度。本文进而提出了基于隐反馈的类时齐 Markov推荐模型，在真实的 WEB 服务器日志数据上的实验证明，类时齐 Markov 模型具有更好的推荐性能。
전통 Markov 련모형재용호류람행위예측방면체현출교호적성능，단불능흔호적체현출용호적흥취도화소추천적혈면적중요성，인차본문제출류시제 Markov 모형。해모형급불동적유별용호단독창건시제 Markov 모형，병용시제 Markov 모형적평은분포표정용호적방문흥취화혈면적중요정도。본문진이제출료기우은반궤적류시제 Markov추천모형，재진실적 WEB 복무기일지수거상적실험증명，류시제 Markov 모형구유경호적추천성능。
Markov chain model shows good performance in the user browsing behavior predictions .But it does not work well in reflecting user’s interestingness and the importance of the recommended pages .Therefore ,this paper proposes classified time ho-mogeneous Markov model .The proposed model create a time homogeneous Markov model separately for every different category of users and use the stationary distribution of the time homogeneous Markov model to characterize users ’ access interest and pages’ importance .Then this paper puts forward a classified time homogeneous Markov model for recommendation based on implicit feed -back .The results of experiment with some real WEB server log data show that the proposed model and algorithm have more perfect performance .