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研究生:陳一帆
研究生(外文):Ye-Fun Chen
論文名稱:應用線上顧客產品認知程度與瀏覽行為於個人化商品推薦之研究
論文名稱(外文):A Study on Applying On-Line Consumer Product Knowledge and Browser Behavior for Personalized Product Recommendation
指導教授:林清同林清同引用關係
指導教授(外文):Ching-Torng Lin
學位類別:碩士
校院名稱:大葉大學
系所名稱:資訊管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:75
中文關鍵詞:產品推薦系統瀏覽者行為產品知識瀏覽行為分析模式
外文關鍵詞:product recommendation systembrowser behaviorproduct knowledgebrowsing behavior analysis model
相關次數:
  • 被引用被引用:1
  • 點閱點閱:147
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
目前許多線上產品推薦系統,利用資料探勘技術挖掘顧客特質和交易關聯性,作為商品推薦依據。但資料探勘需龐大的歷史資料,才能準確的推薦;具有相同背景的顧客不一定有相同的喜好。由於顧客對產品知識認知程度,對於購買決策具有重大影響力。本研究應用網路瀏覽者對產品認知程度與瀏覽行為,建構網路瀏覽者產品偏好指數,作為瀏覽者個人化產品推薦依據。並透過網頁問卷回饋的分析結果,修正瀏覽者行為項目之權重值,以期提昇瀏覽者產品偏好指數之準確性。並以3G手機產品為例建構測試系統,以測試推薦產品準確性及瀏覽者滿意度。案經250位瀏覽者測試結果顯示︰成功推薦瀏覽者第一偏好產品(系統推薦也是第一順序)之機率達60.8%;瀏覽者滿意度誤差指數可隨瀏覽人數增加而逐漸縮小(由0.7085縮小至0.49),顯示本分析模式確實有效預測網路瀏覽者之產品偏好,作為個人化商品推薦之用。期望本分析模式,能作為購物網站業者建構商品推薦系統之參考。
Today numerous on-line recommendation systems use data mining tools to find the relation between consumers’ characteristics and product purchase to deal for product recommendation, but data mining needs huge history data on right recommending. Besides, having the same characteristic consumers may having the different e interests. Because consumer product knowledge have great influence upon buying strategic. So this study combine consumer product knowledge and browsing behavior to build product preference index of web browser(PPIWB) as a basis on browser’s personalized product recommendation system. Through the feedback result of website surrey to modify weights of browser behavior items, expect to improve the accuracy of PPIWB. To use as 3G phones to build test system to try out accuracy of product recommending and browser satisfied degrees. Through 250 browsers to test system, the result shows that system success fit in with browsers favor of product reach 60.8%. Through increasing number of browsers to decrease PPIWB the gap of browser satisfaction from 0.7085 reduce to 0.49. The results shows analysis model had effective forecast the product preference of browser. We hope the analysis model of this study can be used as consultation for shopping web business for building product recommendation system.
Key Words︰recommendation system, browser behavior, product knowledge, data mining
目錄

封面內頁
簽名頁
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vi
目錄 vii
圖目錄 ix
表目錄 x

第一章 緒論
第一節 研究背景與動機 1
第二節 研究目的 4
第三節 研究方法 5
第四節 論文架構 5
第五節 研究流程 6
第六節 研究限制 7
第二章 文獻探討
第一節 消費者產品知識 8
第二節 個人化服務 11
第三節 資料探勘 13
第四節 推薦系統 20
第三章 網路使用者行為探勘分析模式
第一節 研究方法與設計 26
第二節 系統功能架構與流程圖 31
第四章 系統測試結果分析
第一節 開發工具與環境 46
第二節 研究對象 46
第三節 可行性評估與探討 51
第五章 結論與後續研究建議
第一節 研究結論 56
第二節 後續研究建議 58
參考文獻 60
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