English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51296/86402 (59%)
Visitors : 8163958      Online Users : 80
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/74580


    Title: 結合顧客輪廓預測會員回訪率
    Other Titles: Combine customer profile for a member's return visit rate prediction
    Authors: 黃立忠;Wong, Lap-Chung
    Contributors: 淡江大學資訊工程學系碩士班
    蔣定安;Chiang, Ding-An
    Keywords: 顧客輪廓;概念漂移;行為定位;Customer profile;concept drift;Behavioral targeting
    Date: 2011
    Issue Date: 2011-12-28 18:56:55 (UTC+8)
    Abstract: 網路廣告服務的收入,是經營入口網路服務業者的主要營收來源之一,而經常造訪網站的會員更是業者的一大資產。其中,如何利用會員在網路上留下的行為紀錄,來增益網站的價值及收入,逐漸成為這些入口網站服務業者重視的議題之一。我們藉由會員過去的網頁瀏覽紀錄及購買紀錄來建立顧客輪廓,並藉此計算出點擊喜好指數(Click Preference Index, CPI),然後結合會員的來訪行為模式,建立預測會員回訪率的模型,稱之為行為興趣模型。我們以某知名入口網站所提供之資訊作為實驗資料,由實驗證明透過此模型能有效鑑別不同回訪潛力之會員,進而尋找出有高回訪潛力之會員。本研究之結果將可提供給入口網站的行銷人員進行適當的行銷策略擬定,並增加與廣告主洽談時有利的談判條件,使入口網站能增加獲利。
    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 construct a Behavioral Preference (BP) model and apply the different types of the past return visit patterns as the basis 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 BP model can be practically implemented and provide adequate results.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML111View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback