MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data
MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data
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DOI：
10.1007/s10618-017-0518-x
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发表时间：
2016-12
影响因子：
4.8
通讯作者：
Martin Becker;F. Lemmerich;Philipp Singer;M. Strohmaier;A. Hotho
中科院分区：
文献类型：
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作者：
Martin Becker;F. Lemmerich;Philipp Singer;M. Strohmaier;A. Hotho
用户数据的连续跟踪经常被在线和离线观察到，例如，作为访问的网站的序列或作为由GPS捕获的位置的序列。然而，理解解释序列数据产生的因素是一项具有挑战性的任务，特别是因为数据生成通常不是同质的。例如，导航行为可能在浏览网站的不同阶段发生变化，或者移动行为可能在用户组之间发生变化。在这项工作中，我们解决了这个任务，并提出了MixedTrails，贝叶斯方法比较的假设的可验证性的生成过程中的异构序列数据。每个假设都来自现有的文献，理论或直觉，并代表了一组状态之间的转移概率的信念，这些状态可以在观察到的转移组之间变化。例如，当试图了解一个城市中的人类运动并给出一些数据时，假设游客比当地人更有可能朝着兴趣点移动的假设可能比假设相反的假设更合理。我们的方法采用了这样的假设，贝叶斯先验的生成混合转移马尔可夫链模型，并利用贝叶斯因子比较其可行性。我们讨论了分析和近似推理方法计算贝叶斯因子的边际似然，给出解释结果的指导，并说明我们的方法与几个实验的合成和经验数据从维基百科和Flickr。因此，这项工作使一种新的分析研究在许多应用领域的序列数据。
Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and proposeMixedTrails, a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.