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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/92491


    Title: Combing customer profiles for members' return visit rate predictions
    Authors: Chen, Chun-Hao;Chiang, Rui-Dong;Wong, Yi-Hsin;Chu, Huan-Chen
    Contributors: 淡江大學資訊工程學系
    Keywords: Behavioral targeting;Customer profile;Concept drift
    Date: 2013-02-01
    Issue Date: 2013-10-16 15:24:39 (UTC+8)
    Publisher: Kumamoto: ICIC International
    Abstract: The major profit of 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 advertisement) 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.
    Relation: International Journal of Innovative Computing, Information and Control 9(2), pp.503-523
    Appears in Collections:[資訊工程學系暨研究所] 期刊論文

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