淡江大學機構典藏:Item 987654321/94406
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62830/95882 (66%)
Visitors : 4048202      Online Users : 592
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/94406


    Title: 超市賣場推薦系統實作
    Other Titles: Design of supermarket recommender system
    Authors: 張淑俐;Chang, Shu-Li
    Contributors: 淡江大學資訊工程學系碩士在職專班
    蔣璿東;Chiang, Rui-Dong
    Keywords: 推薦系統;協同過濾;Recommender System;Collaborative Filtering
    Date: 2013
    Issue Date: 2014-01-23 14:35:30 (UTC+8)
    Abstract: 近年來因電子商務的興起,人們的消費型態也從傳統的實體店面購物,轉變為在網路商店上進行消費,推薦系統在電子商務上的運用很多,也有一定之成效,但對於實體零售業(Retail)而言,由於商家對於顧客消費並沒有設定門檻,無論是不是會員都可以進行商品購買,所以,商家只能針對會員的消費行為進行分析,但對於非會員的部份,卻無法有效掌握。
    本研究是將推薦系統的概念應用在實體零售業上,運用協同過濾最常使用的方法Item-based進行研究,評估推薦系統套用在商品小類推薦之可行性。我們使用某知名企業所提供之資料作業實驗資料集,透過實驗分析結果,驗證本研究將小類別進行推薦是可行的,我們希望透過推薦系統的幫助,能增加商品的銷售,使消費者產生依賴及提高忠誠度,減少顧客流失。對廠商而言,也能更有效掌握消費者所購買的類別,並透過共同行銷、資訊交叉運用、產品組合等行銷策略,增加消費者下一次購買的可能性,吸引與留住顧客,提高市場佔有率。
    因此,本研究的推薦方法能夠提供零售業做為行銷分析的基礎,轉換消費型態與思維,使會員能確切的獲得合適的推薦,超市進而增加營運與獲利。
    Due to the rise of e-commerce in recent years, people’s consumption patterns have also changed from traditional physical storefront shopping to online shopping consumption. However, TRhe recommender system has a lot of applications in e-commerce and has also had some measure of success, but in physical retail, business firms never set the threshold for consumer spending and disregard whether or not members are able to purchase goods. Thus, business firms can only analyze members’ consumer behavior, but as non-members cannot be effectively controlled.

    This research on the conceptual application of the recommender system in physical retail was conducted using the item-based method often seen in collaborative filtering, and the feasibility of indiscriminately applying the recommender system in the small categories of recommended commodities was evaluated. We use a well-known enterprise data operations provided experimental data sets,it is hoped that the sales of the commodities can be strengthened with the help of the recommender system, thereby generating consumers’ reliance, improving the degree of loyalty, and reducing the loss o f customers. In terms of the manufacturers, all the categories of consumers’ purchases can also be even more effectively grasped, and the common marketing, information cross-application, product integration, and other marketing strategies will increase consumers’ subsequent purchase possibilities, attract and keep customers, and increase the market share.

    Therefore, the recommendation methods of this research can adequately provide retail a basis for marketing analysis, transform consumption patterns and thinking, and enable members to exactly obtain the appropriate recommendations, thus strengthening supermarkets’ operations and profits.
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

    Files in This Item:

    File SizeFormat
    index.html0KbHTML175View/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