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融合停留时间的隐Markov个性化推荐模型 被引量：2

Hidden Markov model fused with staying time for personalized recommendation
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摘要 静态模型在推荐系统中往往将用户的兴趣偏好看作是固定不变的,而在一定程度上与实际并不符合。为此,基于隐Markov动态模型提出一种融合停留时间的类时齐隐Markov个性化推荐模型(ctqHMM)。该模型用隐含状态变量的转移来模拟Web用户的兴趣变迁,并用停留时间来描述用户对某一偏好感兴趣的程度和所推荐页面的重要性。然后,提出一种基于该模型平稳分布的用户聚类方法,并将其用于推荐系统中。在真实的Web服务器访问记录数据上的实验证明,类时齐隐Markov模型具有更好的推荐性能。 Static model in the recommendation system often regards the user＇s interest as changeless,which is inconsistent with the actual to a certain extent.With regards to this,a hidden Markov model fused with staying time for personalized recommendation （ctqHMM） based on the HMM dynamic model is proposed.The proposed model employs the transfer of the implicit state variables to simulate the changes of Web users＇ interests,aad uses staying time to describe the level of interest to the specific preference and the importance of the recommended pages.Then,a user＇s clustering method based on the stationary distribution of the ctqHMM is also proposed and applied into the recommending systems.Experiment results on real Web server access log data show the encouraging performance of the proposed method over the state-of-the-arts.
作者 刘胜宗 樊晓平 廖志芳 胡佳
机构地区 中南大学信息科学与工程学院 湖南财政经济学院网络化系统研究所 中南大学软件学院
出处 《通信学报》 EI CSCD 北大核心 2014年第9期112-121,共10页 Journal on Communications
基金 国家科技支撑计划基金资助项目(2012BAH08B00) 国家自然科学基金资助项目(61073105) 湖南省自然科学基金资助项目(12JJ3074) 湖南省科技支撑计划基金资助项目(2012GK4006)~~
关键词 WEB挖掘 类时齐隐Markov模型 平稳分布 用户聚类 个性化推荐 HMM Web mining classified time homogeneous hidden Markov model stationary distribution user clustering personalized recommendation HMM
分类号 TN911.23 [电子电信—通信与信息系统]
  • 相关文献

参考文献17

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二级参考文献104

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共引文献840

同被引文献18

  • 1Awad M A, Khalil I. Prediction of User's Web-browsing Behavior: Application of Markov Model [ J ]. IEEE Transactions on Systems, Man, and Cybernetics, Part B : Cybernetics, 2012,42 ( 4 ) :1131-1142.
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通信学报
2014年 第9期

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