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Predicting Purchase Proneness of Anonymous User in Mobile Commerce

机译：预测移动商务中匿名用户的购买倾向

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摘要

In recent years, mobile commerce is developing rapidly because of the popularity of mobile devices.However, for the difficulty of the mobile device input, the users of the e-commerce websites usually don't log on the website when they are browsing, which resulting in a situation that a large number of website visitors are anonymous users.In order to increase sales revenue and expand market share, an effective prediction of anonymous users' purchases proneness is very helpful in providing targeted marketing strategy for website to induce anonymous users to purchase.In the past, customer segmentation was mainly analyzed and modeled by customers' historical data.But the history data of anonymous users can't be obtained on mobile commerce sites.This method is difficult to put into management practice.In order to solve this problem, this paper proposes a method based on random forest of using user clickstream data to forecast purchase proneness in real time.This method includes two stages: the model training part and the user purchasing proneness prediction part.In the model training part, a classifier based on random forest algorithm is trained.In the users' predicting part, the classifier is used to predict the user's purchase proneness in real time.The method proposed can be effectively applied in the real-time prediction of anonymous users' purchasing proneness, and the results of prediction will help enterprises implement the marketing measures in real time.
机译：近年来，由于移动设备的普及，移动商务发展迅速。然而，由于移动设备输入的困难，电子商务网站的用户浏览时通常不登录该网站，为了增加销售收入和扩大市场份额，有效地预测匿名用户的购买倾向对提供有针对性的网站营销策略以诱使匿名用户过去，客户细分主要是根据客户的历史数据进行分析和建模，但是匿名用户的历史数据无法在移动商务站点上获取，这种方法难以付诸于管理实践。针对这一问题，本文提出了一种基于随机森林的方法，该方法利用用户点击流数据实时预测购买倾向。该方法包括两个阶段：模型训练部分和用户购买倾向预测部分。在模型训练部分中，训练基于随机森林算法的分类器;在用户预测部分中，使用分类器实时预测用户的购买倾向。该方法可有效地应用于匿名用户购买倾向的实时预测，预测结果将有助于企业实时实施营销手段。

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