運用時間關係預測會員回訪率

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Thesis > Item 987654321/74562

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/74562

Title:	運用時間關係預測會員回訪率
Other Titles:	Prediction of a member's return visit rate using the time factor
Authors:	楊晴涵;Yang, Ching-Han
Contributors:	淡江大學資訊工程學系資訊網路與通訊碩士班蔣定安;Chiang, Ding-An
Keywords:	行為定位;顧客輪廓;時間函數;概念飄移;Behavioral targeting;Customer profile;Time function;concept drift
Date:	2011
Issue Date:	2011-12-28 18:53:24 (UTC+8)
Abstract:	在台灣各項網路服務之廣告及電子商務，是入口網站主要的獲利方式，如何利用使用者在網路上留下的行為足跡，預測使用者對於廣告可能的回響程度來決定其因應的廣告行銷策略，然而使用者興趣會隨著時間變化而改變，為了掌握會員在不同時間點的興趣差異，本篇論文利用概念飄移(Concept Drift)的概念，依據不同類型會員來降低或提高這些會員過去歷史紀錄的影響程度，建構考慮時間因素之點擊興趣指數(Click Preference Index with Time factor, CPIT)，透過此模型有效鑑別不同行為之會員，精準找尋到高回訪潛力之會員。我們以某知名入口網站所提供的資訊作為實驗資料，由實驗證明CPIT 模型確實精準找尋到高回訪潛力之會員，以供入口網站的行銷人員有效益的行銷策略，進而增加獲利。 The profit of portal companies in Taiwan is generated by the online advertising and e-commerce. Effective advertising requires predicting how a user responds to an advertisement and then targeting (presenting the advertisements) to reflect the users’ favor. The behavioral target leverages historical users’ behaviors in order to select the ads which are most related to the users to display. Although we would not like to provide advertisements to customers, with the same concept, we would expect to predict return visit rates for the registered members in the specific category of the portal site. However, customers’ preferences change over time. In order to capture the concept drift, we propose a novel and simple time function to increase/decrease the weight of the old data to various members’ past behaviors. Then, we construct a member’s click preference index with time factor(CPIT) model, to effectively distinguish the different kinds of member behaviors and predict the members’ return visit rates. The marketers of a portal site can target the members with high return visit rates and design the corresponding marketing strategies. The experimental results with a real dataset have demonstrated that the CPIT model can be practically implemented and provide adequate results.
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

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