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研究生:許益誠
研究生(外文):Yi-Chen Hsu
論文名稱:電子目錄上推薦服務之研究
論文名稱(外文):A Study of Recommedation Service on E-Catalog
指導教授:林熙禎林熙禎引用關係
指導教授(外文):Shi-Jen Lin
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊管理研究所
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:2002
畢業學年度:90
語文別:中文
論文頁數:79
中文關鍵詞:電子商務電子目錄個人化網站探勘推薦服務
外文關鍵詞:E-commerceE-catalogWeb MiningRecommedation servicePersonalization
相關次數:
  • 被引用被引用:2
  • 點閱點閱:123
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隨著電子商務的發展,不論是B2B或者是B2C的商業模式,電子目錄儼然已經扮演與使用者溝通的主要介面。電子目錄不再只是提供商品的規格,更是提供客戶服務的重要媒介。然而,一個擁有豐富資訊的網站,如何對於不熟悉網站架構的使用者,或對購買商品特性不熟悉的使用者,協助他們做採購的決策呢?
本論文試圖透過網站探勘技術,瞭解使用者的瀏覽目的,並將此探勘所得到的瀏覽樣式,推薦給具有相同需求的使用者做為參考。同時,將推薦分為同類熱門商品、相關產品、以及其他產品資訊說明等方式推薦,讓使用者更清楚推薦原因。
本論文以網站探勘的技術為基礎,提出概念限制型參考(CCR,Conceptual Constrained Reference)的交易識別方式,確認交易為某類資訊的瀏覽,藉此確認使用者目的。之後,利用改良的混合型順序性(MixSEQ)相似度比對,對瀏覽路徑做叢集(Clustering)處理,作為推薦的資料集。在推薦策略方面,先將網頁做分類,區分為內容型網頁與導覽型網頁,推薦時也以內容為主要的推薦網頁,以提高推薦實用性。
As the E-Commerce prevalence,E-catalog has become an important interface to a company thorugh which customers interact, regardless of B2B or B2C. E-catalog not only provides product information,but becomes the important media of service providing for customers. However,how does a E-catalog assist casual/unfamiliar users with such rich information in their purchasing decision making?
The challege for WebSites is how we know uesrs needs? Thus, we will apply Web Mining technology to undestand users needs and get usage pattern from previous browsing expereince.
In this thesis,we propose a new transaction identification, Conceptual-Constrained Reference(CCR),for understanding what the goal of users in browsing the e-catalong.Furthermore,we use the MixSEQ similairty measure for web-usage clustering and clusters will be the recommedation dataset.Finally,we will calssify pages into two types-CONTENT PAGE and Navigation Page,and recommed those pages which are content types for users when they are browsing.
第一章 緒論1
1-1研究動機與背景1
1-2研究目的2
1-3研究範圍2
1-4研究流程3
1-5論文架構5
第二章 相關研究6
2-1電子目錄需求分析6
2-1-1電子目錄的角色6
2-1-2電子目錄的功能8
2-1-3個人決策資訊需求理論10
2-1-4企業採購的資訊需求分析12
2-1-5小結14
2-2網站推薦服務之研究15
2-2-1網站如何提供個人化的服務?15
2-2-2個人化瀏覽協助的相關技術18
2-2-2-1以資訊內容為主的資訊過濾19
2-2-2-2以合作式為主的個人偏好過濾20
2-2-2-3利用網站探勘技術作個人化推薦服務21
第三章 研究架構34
3-1研究假設34
3-2名詞定義35
3-3系統架構37
3-3-1系統處理階段37
3-3-2系統實作41
3-4系統流程說明41
3-4-1網站模式化42
3-4-1-1電子目錄定義42
3-4-1-2 網站架構粹取42
3-4-1-3 網頁分類43
3-4-2使用者存取記錄處理44
3-4-2-1 資料清除44
3-4-2-2 User Session識別44
3-4-2-3利用網站架構做補頁45
3-4-2-4利用概念限制做交易識別47
3-4-3相似度比對52
3-4-4資料結構52
3-4-4-1 Session Model52
3-4-4-2 Cluster Model55
3-4-4-3 Active User Session56
3-4-5叢集處理56
3-4-6推薦方法57
第四章 實驗與討論60
4-1模擬環境設計60
4-2虛擬資料集建立61
4-2-1參數說明61
4-2-2實驗參數設定62
4-3實驗設計63
4-3-1實驗方法63
4-4評估標準說明63
4-5實驗結果比較與分析64
4-5-1MFR與CCR交易識別方式之預測品質比較64
4-5-2順序型編號與混合型編號之預測品質比較66
4-5-3綜合比較68
第五章 結論與未來方向70
5-1研究結論與成果70
5-2未來研究方向與建議71
參考文獻74
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