Detecting memory and structure in human navigation patterns using Markov chain models of varying order.
Detecting memory and structure in human navigation patterns using Markov chain models of varying order.
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DOI：
10.1371/journal.pone.0102070
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Strohmaier M
中科院分区：
文献类型：
--
作者：
Singer P;Helic D;Taraghi B;Strohmaier M
用于理解人类在Web上导航的最常用模型之一是马尔可夫链模型，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。人们普遍认为，人类在网络上的导航满足无记忆马尔可夫属性，即用户访问的下一个页面只取决于当前页面，而不取决于以前访问过的页面。这个想法已经在许多应用程序中找到了自己的方式，比如b谷歌的PageRank算法等。最近，新的研究表明，人类导航可能更好地使用高阶马尔可夫链模型，即，下一页取决于过去点击的更长的历史。然而，这一发现是初步的，并没有考虑到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的原因。在这项工作中，我们彻底提出了各种先进的推理方法来确定适当的马尔可夫链顺序。我们强调了每种方法的优缺点，并将它们应用于研究人类在Web上导航的记忆和结构。我们的实验表明，高阶模型的复杂性比它们的实用性增长得更快，因此我们确认无内存模型代表了一个相当实用的页面级人类导航模型。然而，当我们将分析扩展到主题层面时，我们从特定的页面过渡抽象到主题之间的过渡，我们发现无记忆假设被违反了，并且可以观察到特定的规律。我们报告了两种类型的导航数据集（目标导向和自由形式）的实验结果，并观察到有趣的结构差异，这为未来工作中更多的人类导航上下文研究提供了强有力的论据。
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.
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影响因子：
2.5
作者：
KATZ, RW
通讯作者：
KATZ, RW
影响因子：
--
作者：
KULLBACK, S;LEIBLER, RA
通讯作者：
LEIBLER, RA
影响因子：
4.8
作者：
Cadez, I;Heckerman, D;White, S
通讯作者：
White, S
DOI：
10.1109/tkde.2007.1012
发表时间：
2007-04-01
影响因子：
8.9
作者：
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通讯作者：
Levene, Mark
影响因子：
56.9
作者：
Huberman, BA;Pirolli, PLT;Lukose, RM
通讯作者：
Lukose, RM