電子目錄上推薦服務之研究

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姓名

許益誠(Yi-Chen Hsu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

電子目錄上推薦服務之研究
(A Study of Recommedation Service on E-Catalog)

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摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 電子商務
★ 電子目錄
★ 個人化
★ 網站探勘
★ 推薦服務

關鍵字(英)

★ E-commerce
★ E-catalog
★ Web Mining
★ Recommedation service
★ Personalization

論文目次

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 Model 52
3-4-4-2 Cluster Model 55
3-4-4-3 Active User Session 56
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
4-5-1 MFR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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指導教授

林熙禎(Shi-Jen Lin)

審核日期

2002-7-5

推文