Awad M A, Khalil I
IEEE Trans Syst Man Cybern B Cybern. 2012 Aug;42(4):1131-42. doi: 10.1109/TSMCB.2012.2187441. Epub 2012 Mar 2.
Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user's behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all- Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all- Kth model.
网页预测是一个分类问题，在这个问题中，我们试图根据用户先前访问过的网页信息来预测用户接下来可能访问的网页集合。在为用户提供网络服务时预测用户行为可有效地应用于各种关键应用程序中。此类应用程序在建模复杂性和预测准确性之间存在传统的权衡。在本文中，我们分析和研究了网页预测中的马尔可夫模型和全K阶马尔可夫模型。我们提出了一种新的改进型马尔可夫模型，以缓解路径数量方面的可扩展性问题。此外，我们提出了一种新的两层预测框架，该框架基于训练示例和生成的分类器创建一个示例分类器EC。我们表明，这样的框架可以在不影响预测准确性的情况下提高预测时间。我们使用标准基准数据集，通过马尔可夫模型的变体和关联规则挖掘来分析、比较和证明我们技术的有效性。我们的实验表明，我们改进的马尔可夫模型在不影响准确性的情况下减少路径数量方面是有效的。此外，结果支持我们的分析结论，即全K阶模型的阶数越高，准确性越高。
