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COMBING CUSTOMER PROFILES FOR MEMBERS' RETURN VISIT RATE PREDICTIONS

机译:梳理客户个人资料,以实现会员的回访率预测

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摘要

The major profit of portal companies in Taiwan is generated by online advertising and e-commerce. Effective advertising requires predicting how a user responds to an advertisement and then targeting (presenting the advertisements) to reflect users' favor. As customers' preferences may change over time, we take the different types of past behavior patterns of the registered members to capture concept drifts. Then, we combine the click preference index (CPI) and the preference drifts to propose a Behavioral Preference (BP) model, and to predict the members' return visit rates in the specific category of the portal site. The marketers of the portal site can target the registered members with high return visit rates and design corresponding marketing strategies. The experimental results with a real dataset show that our model can effectively predict the registered members' return visit rates.
机译:台湾门户网站公司的主要利润来自在线广告和电子商务。有效的广告需要预测用户如何响应广告,然后进行定位(呈现广告)以反映用户的偏爱。由于客户的偏好可能会随着时间而变化,因此我们采用注册会员过去的行为模式的不同类型来捕获概念上的偏差。然后,我们结合点击偏好指数(CPI)和偏好漂移来提出行为偏好(BP)模型,并预测门户网站特定类别中的成员回访率。门户网站的营销人员可以针对具有较高回访率的注册会员,并设计相应的营销策略。真实数据集的实验结果表明,我们的模型可以有效地预测注册会员的回访率。

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  • 作者单位

    Department of Computer Science and Information Engineering Tamkang University No. 151, Yingzhuan Rd., Tamsui Dist., New Taipei 25137, Taiwan;

    Department of Computer Science and Information Engineering Tamkang University No. 151, Yingzhuan Rd., Tamsui Dist., New Taipei 25137, Taiwan;

    Department of Information Management Chang Gung University of Science and Technology No. 261, Wen-hwa 1st Rd., Kwei-shan, Taoyuan, Taiwan;

    Department of Computer Science and Information Engineering Tamkang University No. 151, Yingzhuan Rd., Tamsui Dist., New Taipei 25137, Taiwan;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    behavioral targeting; customer profile; concept drift;

    机译:行为目标;客户资料;概念漂移;

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