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    Title: A rough set-based association rule approach for a recommendation system for online consumers
    Authors: Liao, Shu-hsien;ChangHsiao-ko
    Keywords: Data mining;Rough set;Association rule;Rough set association rule;Analytic hierarchy process;Recommendation systems
    Date: 2016-11-01
    Issue Date: 2016-08-15
    Publisher: Elsevier Ltd
    Abstract: Increasing use of the Internet gives consumers an evolving medium for the purchase of products and services and this use means that the determinants for online consumers’ purchasing behaviors are more important. Recommendation systems are decision aids that analyze a customer's prior online purchasing behavior and current product information to find matches for the customer's preferences. Some studies have also shown that sellers can use specifically designed techniques to alter consumer behavior. This study proposes a rough set based association rule approach for customer preference analysis that is developed from analytic hierarchy process (AHP) ordinal data scale processing. The proposed analysis approach generates rough set attribute functions, association rules and their modification mechanism. It also determines patterns and rules for e-commerce platforms and product category recommendations and it determines possible behavioral changes for online consumers.
    Relation: Information Processing & Management 52(6), p.1142–1160
    DOI: 10.1016/j.ipm.2016.05.003
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

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