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A Sequential Pattern Analysis for Dynamic Discovery of Customers' Preference  

Song, Ki-Ryong (THEFACESHOP)
Noh, Soeng-Ho (Department of e-Business, Korea Polytechnic University)
Lee, Jae-Kwang (Department of e-Business, Korea Polytechnic University)
Choi, Il-Young (School of Business Adminstration, KyungHee University)
Kim, Jae-Kyeong (School of Business Adminstration, KyungHee University)
Publication Information
Information Systems Review / v.10, no.2, 2008 , pp. 195-209 More about this Journal
Abstract
Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.
Keywords
Recommender; SOM; Store Management; Sequential Patter; Cluster Analysis;
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