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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/127204
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/127204

    Title: 零售商業分析:購物籃資料的指數隨機圖模型
    Retail Business Analytics: Exponential Random Graph Modeling of Market Basket Data
    Authors: 張月馨
    Chang, Yueh-Hsin
    Contributors: 莊皓鈞

    Chuang, Hao-Chun
    Chou, Yen-Chun

    Chang, Yueh-Hsin
    Keywords: 零售業
    Dyadic Dependence Model
    Product Network
    Date: 2019
    Issue Date: 2019-11-06 15:25:01 (UTC+8)
    Abstract: 購物籃分析在當代的零售商業分析扮演重要的角色,可以幫助零售商了解消費者之購物傾向,但是購物籃分析缺乏一般化的規則解釋商品彼此併買的潛在原因,因此本研究採用指數隨機圖模型(Exponential Random Graph Modeling, ERGM)解決購物籃分析對商品連結缺乏解釋性的限制。指數隨機圖模型是用來檢測隨機圖或是網路圖模型中彼此連結關係模式的工具,對欲解釋之網路圖結構特徵提供良好的分析方法。本研究主要探討如何應用超商零售業之交易資料,設計一套以指數隨機圖模型為基礎,加入結構特徵之二元依賴模型(Hunter, Handcock, Butts, Goodreau, & Morris, 2008)之分析應用流程,幫助零售業者對行銷策略提供更好的應用方向。
    Nowadays, market basket analysis plays an important role in retail business analysis, as it allows the retailer to develop a better understanding of consumers’ purchasing tendency. However, market basket analysis lacks general rules to explain the potential reasons why the products are bought together. Therefore, this research uses Exponential Random Graph Model (ERGM) to enhance the explanatory power on discovered co-purchase relationships. The ERGM is a technique for assessing interdependencies between nodes in random graphs or networks, and it enables analysts to uncover structural features in networks. With more than three million transaction records of a leading convenience store in Taiwan, our research focuses on how to model these transaction data using ERGM and combines the Dyadic Dependence Model (Hunter, Handcock, Butts, Goodreau, & Morris, 2008) to design a new analysis process. The proposed process is aimed at guiding retailers to develop better marketing strategies regarding bundle selling/co-purchase.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106356002
    Data Type: thesis
    DOI: 10.6814/NCCU201901207
    Appears in Collections:[資訊管理學系] 學位論文

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