Evaluating variable-length Markov chain models for analysis of user Web navigation sessions
Evaluating variable-length Markov chain models for analysis of user Web navigation sessions
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DOI：
10.1109/tkde.2007.1012
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发表时间：
2007-04-01
影响因子：
8.9
通讯作者：
Levene, Mark
中科院分区：
文献类型：
--
作者：
Borges, Jose;Levene, Mark
马尔可夫模型已被广泛用于表示和分析用户Web导航数据。在以前的工作中，我们已经提出了一种方法来动态地扩展马尔可夫链模型的顺序和一个互补的方法来评估这样一个可变长度的马尔可夫链的预测能力。在这里，我们回顾这两种方法，并提出了一种新的方法来衡量一个可变长度的马尔可夫模型，总结用户Web导航会话到给定的长度的能力。虽然模型的总结能力对于识别用户导航模式很重要，但是为了预测用户在跟踪给定的踪迹之后的下一个链接选择，例如个性化网站，进行预测的能力也很重要。我们提出了一个广泛的实验评估提供了有力的证据，预测精度线性增加的摘要能力。
Markov models have been widely used to represent and analyze user Web navigation data. In previous work, we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable-length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable-length Markov model to summarize user Web navigation sessions up to a given length. Although the summarization ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalize a Web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarization ability.