HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web
HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web
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DOI：
10.1145/2736277.2741080
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
Philipp Singer;D. Helic;A. Hotho;M. Strohmaier
中科院分区：
文献类型：
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作者：
Philipp Singer;D. Helic;A. Hotho;M. Strohmaier
今天，当用户与Web交互时，他们会大规模地留下连续的数字痕迹。此类人类轨迹的例子包括网络导航、在线餐厅评论序列或在线音乐播放列表。了解驱动这些轨迹产生的因素对于改善底层网络结构、预测用户点击或增强推荐等都是有用的。在这项工作中，我们提出了一种称为HypTrails的通用方法，用于比较关于Web上人类轨迹的一组假设，其中假设表示关于状态之间转换的信念。我们的方法利用带有贝叶斯推理的马尔可夫链模型。主要思想是将假设作为信息Dirichlet先验，并利用贝叶斯因子对先验的敏感性来比较假设。为了从假设中引出狄利克雷先验，我们提出了一种所谓的（试验）轮盘赌方法的改编。我们通过以下实验展示了HypTrails的一般机制和适用性：(i)我们控制产生它们的机制的合成路径和（ii）来自不同领域的经验路径，包括网站导航，商业评论和在线音乐播放。我们的工作扩展了在网络上研究人类足迹的可用方法。
When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.