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題名 A Preference Scoring Technique for Personalized Advertisements on Internet Storefronts
作者 Kim,J. W.;Lee H.;M. J. Shaw;張欣綠;M. Nelson
Chang, Hsin-Lu
關鍵詞 Internet storefront; Customization; Personalization; Recommendation techniques; Electronic commerce
日期 2006-02
上傳時間 17-Jan-2009 16:08:29 (UTC+8)
摘要 This paper describes a new personalized advertisement selection technique based on a customer’s preference scores for product categories. This method performs well, despite having low data and analysis requirements, relative to other methods in use. Customer preference scores are updated based on a customer’s initial profile, purchase history, and behavior in an Internet storefront, and are then used to select and display appropriate advertisements on Internet web pages when the customer visits the Internet storefront. Compared with currently available recommendation techniques such as collaborative filtering or rule-based methods, preference scoring techniques use only a single customer’s data to select appropriate advertisements and do not require a learning data set, and yet have competitive performance and can reflect changes in a customers’ preference. An experiment is performed to compare two alternative data storage structures, the preference table and the preference tree, with random selection and collaborative filtering.
關聯 Mthematical and Computer Modelling, 44(1/2), 3-15
資料類型 article
DOI http://dx.doi.org/10.1016/j.mcm.2004.12.011
dc.creator (作者) Kim,J. W.;Lee H.;M. J. Shaw;張欣綠;M. Nelsonen_US
dc.creator (作者) Chang, Hsin-Lu-
dc.date (日期) 2006-02en_US
dc.date.accessioned 17-Jan-2009 16:08:29 (UTC+8)-
dc.date.available 17-Jan-2009 16:08:29 (UTC+8)-
dc.date.issued (上傳時間) 17-Jan-2009 16:08:29 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/27079-
dc.description.abstract (摘要) This paper describes a new personalized advertisement selection technique based on a customer’s preference scores for product categories. This method performs well, despite having low data and analysis requirements, relative to other methods in use. Customer preference scores are updated based on a customer’s initial profile, purchase history, and behavior in an Internet storefront, and are then used to select and display appropriate advertisements on Internet web pages when the customer visits the Internet storefront. Compared with currently available recommendation techniques such as collaborative filtering or rule-based methods, preference scoring techniques use only a single customer’s data to select appropriate advertisements and do not require a learning data set, and yet have competitive performance and can reflect changes in a customers’ preference. An experiment is performed to compare two alternative data storage structures, the preference table and the preference tree, with random selection and collaborative filtering.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Mthematical and Computer Modelling, 44(1/2), 3-15en_US
dc.subject (關鍵詞) Internet storefront; Customization; Personalization; Recommendation techniques; Electronic commerce-
dc.title (題名) A Preference Scoring Technique for Personalized Advertisements on Internet Storefrontsen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.mcm.2004.12.011en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.mcm.2004.12.011en_US