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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/99933

    Title: Mining Customer Knowledge for a Recommendation System in Convenience Stores
    Authors: Liao, S. H.;Wen, C. H.;Hsiao, P.Y.;Li, C. W.;Hsu, C. W.
    Contributors: 淡江大學管理科學學系
    Date: 2014-06-01
    Issue Date: 2015-01-14 12:09:29 (UTC+8)
    Publisher: Hershey: I G I Global
    Abstract: Taiwan's rapid economic growth with increasing personal income leads increasing numbers of young unmarried people to eat out, and shopping at convenience stores for food is indispensable to the lives of these people. Thus, it is an essential issue for convenience store owners to know how to accurately market appropriate products and to choose effective endorsers for brands or products in order to attract target consumers. Data mining is a business intelligence analysis approach with great potential to help businesses focus on the most important business information contained in a database. Therefore, this study uses the Apriori algorithm as an association rules approach, and clustering analysis for data mining. The authors divide consumers into three groups by their consumer profiles and then find each group's product preference mixes, product endorsers, and product/brand line extensions for new product development. These are developed as a recommendation system for 7-11 convenience stores in Taiwan.
    Relation: International Journal of Data Warehousing and Mining 10(2), pp.55-86
    DOI: 10.4018/ijdwm.2014040104
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

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