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Generating Dynamic Higher-Order Markov Models in Web Usage Mining

机译:在Web用法挖掘中生成动态高阶马尔可夫模型

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摘要

Markov models have been widely used for modelling users' web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter.
机译:马尔可夫模型已广泛用于对用户的Web导航行为进行建模。在先前的工作中,我们提出了一个基于动态聚类的马尔可夫模型,该模型准确地表示了导航会话集合所给出的二阶过渡概率。在此,我们提出了一种考虑了高阶条件概率的方法的概括。该方法利用状态克隆概念以及聚类技术来分离显示条件概率差异的导航路径。我们报告了使用三个真实世界数据集进行的实验。结果表明,某些页面需要很长的历史才能了解用户对链接的选择,而其他页面仅需要很短的历史。我们还表明,可以通过概率阈值参数控制该方法引发的其他状态的数量。

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