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題名 Application of Decision Tree Induction Techniques to Personalized Adevertisements on Internet Storefront
作者 Kim J. W.;Lee B. H.;M. J. Shaw;Chang,Hsin-Lu
     ;Nelson M.
張欣綠
日期 2001-03
上傳時間 17-Jan-2009 16:01:41 (UTC+8)
摘要 Customization and personalization services are a critical success factor for Internet stores and Web service providers. This paper studies personalized recommendation techniques that suggest products or services to the customers of Internet storefronts based on their demographics or past purchasing behavior. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and rule-based matching. In the rule-based approach to personalized recommendation, marketing rules for personalization are usually obtained from marketing experts and used to perform inferencing based on customer data. However, it is difficult to extract marketing rules from marketing experts, and to validate and maintain the constructed knowledge base. This paper proposes a marketing rule-extraction technique for personalized recommendation on Internet storefronts using machine learning techniques, and especially decisiontree induction techniques. Using tree induction techniques, data-mining tools can generate marketing rules that match customer demographics to product categories. The extracted rules provide personalized advertisement selection when a customer visits an Internet store. An experiment is performed to evaluate the effectiveness of the proposed approach with preference scoring and random selection.
關聯 International Journal of Electronic Commerce, 5(3), 45-62
10.1080/10864415.2001.11044215
資料類型 article
dc.creator (作者) Kim J. W.;Lee B. H.;M. J. Shaw;Chang,Hsin-Lu
     ;Nelson M.		en_US
dc.creator (作者) 張欣綠-
dc.date (日期) 2001-03en_US
dc.date.accessioned 17-Jan-2009 16:01:41 (UTC+8)-
dc.date.available 17-Jan-2009 16:01:41 (UTC+8)-
dc.date.issued (上傳時間) 17-Jan-2009 16:01:41 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/27006-
dc.description.abstract (摘要) Customization and personalization services are a critical success factor for Internet stores and Web service providers. This paper studies personalized recommendation techniques that suggest products or services to the customers of Internet storefronts based on their demographics or past purchasing behavior. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and rule-based matching. In the rule-based approach to personalized recommendation, marketing rules for personalization are usually obtained from marketing experts and used to perform inferencing based on customer data. However, it is difficult to extract marketing rules from marketing experts, and to validate and maintain the constructed knowledge base. This paper proposes a marketing rule-extraction technique for personalized recommendation on Internet storefronts using machine learning techniques, and especially decisiontree induction techniques. Using tree induction techniques, data-mining tools can generate marketing rules that match customer demographics to product categories. The extracted rules provide personalized advertisement selection when a customer visits an Internet store. An experiment is performed to evaluate the effectiveness of the proposed approach with preference scoring and random selection.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) International Journal of Electronic Commerce, 5(3), 45-62en_US
dc.relation (關聯) 10.1080/10864415.2001.11044215-
dc.title (題名) Application of Decision Tree Induction Techniques to Personalized Adevertisements on Internet Storefronten_US
dc.type (資料類型) articleen