用变阶马尔可夫链模型检测人类导航模式的记忆和结构
Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order
摘要：
马尔可夫链模型（Markov chain model）是理解人类在Web上导航最常用的模型之一，其中Web页面表示为状态，超链接表示为从一个页面导航到另一个页面的概率。主要是，人类在Web上的导航被认为满足无记忆马尔可夫性质，即用户访问的下一个页面只取决于她当前的页面，而不是以前访问过的页面。这个想法已经在许多应用程序中找到了它的方式，例如Google的PageRank算法和其他算法。最近，新的研究表明，人类导航可以更好地使用高阶马尔可夫链模型建模，即，下一页取决于过去点击的更长历史。然而，这一发现是初步的，并没有考虑高阶马尔可夫链模型的更高复杂性，这就是为什么无记忆模型仍然被广泛使用。在这项工作中，我们彻底提出了一系列先进的推理方法，以确定适当的马尔可夫链顺序。我们强调每种方法的优点和缺点，并将其应用于研究人类在Web上导航的记忆和结构。我们的实验表明，高阶模型的复杂性增长速度比他们的效用，因此，我们确认，无记忆模型代表了一个非常实用的模型，人类导航的页面级别。然而，当我们将分析扩展到主题级别时，我们从特定的页面转换抽象到主题之间的转换，我们发现违反了无记忆假设，并且可以观察到特定的转换。我们报告了两种类型的导航数据集（目标导向与自由形式）的实验结果，并观察到有趣的结构差异，为未来的工作中更多的人类导航上下文研究提供了有力的论据。
英文摘要：
One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
