English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 56552/90363 (63%)
造访人次 : 11831253      在线人数 : 118
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107001

    题名: A rough set-based association rule approach for a recommendation system for online consumers
    作者: Liao, Shu-hsien;ChangHsiao-ko
    关键词: Data mining;Rough set;Association rule;Rough set association rule;Analytic hierarchy process;Recommendation systems
    日期: 2016-11-01
    上传时间: 2016-08-15
    出版者: Elsevier Ltd
    摘要: 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.
    關聯: Information Processing & Management 52(6), p.1142–1160
    DOI: 10.1016/j.ipm.2016.05.003
    显示于类别:[管理科學學系暨研究所] 期刊論文


    档案 描述 大小格式浏览次数
    A rough set-based association rule approach for a recommendation system for online consumers.pdf788KbAdobe PDF0检视/开启



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈