基于隐反馈的类时齐 Markov 推荐模型

摘要

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。%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 .