關鍵字查詢 | 類別:期刊論文 | | 關鍵字:Combing customer profiles for members' return visit rate predictions

[第一頁][上頁]1[次頁][最末頁]目前在第 1 頁 / 共有 02 筆查詢結果
序號 學年期 教師動態
1 101/2 資工系 陳俊豪 教授 期刊論文 發佈 Combing customer profiles for members' return visit rate predictions , [101-2] :Combing customer profiles for members' return visit rate predictions期刊論文Combing customer profiles for members' return visit rate predictionsChen, Chun-Hao; Chiang, Rui-Dong; Wong, Yi-Hsin; Chu, Huan-Chen淡江大學資訊工程學系Kumamoto: ICIC InternationalInternational Journal of Innovative Computing, Information and Control 9(2), pp.503-523The 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 regist
2 101/2 資工系 蔣璿東 教授 期刊論文 發佈 Combing customer profiles for members' return visit rate predictions , [101-2] :Combing customer profiles for members' return visit rate predictions期刊論文Combing customer profiles for members' return visit rate predictionsChen, Chun-Hao; Chiang, Rui-Dong; Wong, Yi-Hsin; Chu, Huan-Chen淡江大學資訊工程學系Kumamoto: ICIC InternationalInternational Journal of Innovative Computing, Information and Control 9(2), pp.503-523The 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 regist
[第一頁][上頁]1[次頁][最末頁]目前在第 1 頁 / 共有 02 筆查詢結果