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Analysis and characterization of comparison shopping behavior in the mobile handset domain

机译：手机领域比较购物行为的分析与表征

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In this work we characterize the session-level behavior of users on an Indian mobile phone comparison shopping website. We also correlate the popularity of handset on various news sources to its popularity on the shopping website. There are three aspects to our study: data analysis, correlation between news sources of product information and popularity of a handset, and behavior prediction. We have used KL divergence to show that a time-homogeneous Markov chain is observed when the number of clicks varies from 5 to 30. Our results depict that Markov chain model does not hold in entirety for comparison shopping setting but tells us how far the Markov chain model holds for this setting. Our analysis corroborates intuition that increasing price leads to decrease in popularity. After the strong correlation between various variables and user behavior was found, we predict the users macro (the overall sales of handset) and micro behavior (whether a user will convert or exit the site) using Markov logic networks. Our predictive model validates the intuition that past browsing behavior is an important predictor for future behavior. Methodology of combining data analysis with machine learning is, in our opinion, a new approach to the empirical study of such data sets.
机译：在这项工作中，我们描述了印度手机比较购物网站上用户的会话级行为。我们还将手机在各种新闻来源上的受欢迎程度与其在购物网站上的受欢迎程度相关联。我们的研究涉及三个方面：数据分析，产品信息的新闻来源与手机的普及程度之间的相关性以及行为预测。我们使用KL散度表明，当点击次数从5变为30时，观察到时间均一的马尔可夫链。我们的结果表明，马尔可夫链模型并不完全适用于比较购物环境，但可以告诉我们马尔可夫距离有多远链模型适用于此设置。我们的分析证实了直觉，即价格上涨导致人气下降。在发现各种变量与用户行为之间有很强的相关性之后，我们将使用马尔可夫逻辑网络预测用户宏（手机的整体销售）和微观行为（用户将转换还是退出网站）。我们的预测模型验证了直觉，即过去的浏览行为是未来行为的重要预测因子。在我们看来，将数据分析与机器学习相结合的方法是一种对此类数据集进行实证研究的新方法。

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