電子目錄上推薦服務之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：47

、訪客IP：18.117.229.44

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)

相關論文

★ 網路合作式協同教學設計平台－以國中九年一貫課程為例	★ 內容管理機制於常用問答集(FAQ)之應用
★ 行動多重代理人技術於排課系統之應用	★ 存取控制機制與國內資安規範之研究
★ 信用卡系統導入NFC手機交易機制探討	★ App應用在電子商務的推薦服務-以P公司為例
★ 建置服務導向系統改善生產之流程-以W公司PMS系統為例	★ NFC行動支付之TSM平台規劃與導入
★ 關鍵字行銷在半導體通路商運用-以G公司為例	★ 探討國內田徑競賽資訊系統－以103年全國大專田徑公開賽資訊系統為例
★ 航空地勤機坪作業盤櫃追蹤管理系統導入成效評估—以F公司為例	★ 導入資訊安全管理制度之資安管理成熟度研究－以B個案公司為例
★ 資料探勘技術在電影推薦上的應用研究-以F線上影音平台為例	★ BI視覺化工具運用於資安日誌分析—以S公司為例
★ 特權帳號登入行為即時分析系統之實證研究	★ 郵件系統異常使用行為偵測與處理-以T公司為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

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

參考文獻

[1] Aberdeen Group, Inc., “Interactive Customer Care： Enriching the Self-Service Experience with Automated Agents,” http://www.Aberdeen.com, November 2000.
[2] Baeze-Yates, R. and Ribeiro-Neto, B., “Modern Information Retrieval,” ACM Press Book, Addison Wesley, 1999.Balabanvoic, M. and Shoham, Y., “Combining Content-Based and Collaborative Recommendation,” Communications of the ACM Vol.40, No.3, March 97.
[4] Banerjee, A. and Ghosh, J., “Concept-based Clustering of Clickstream Data,” In Proc. 3rd Intl. Conf. on Information Technology, Dec 2000, pages p.145-p.150.
[5] Baron, J. P., Shaw, M. J. and Bailey, Jr. A. D., “Electronic Catalogs in the Web-Based Business-to-Business Procurement Process,” In Handbook on Electronic Commerce, edited by M. Shaw, Springer Press.
[6] Baron, J. P., Shaw, M. J. and Bailey, Jr. A. D., “Web-based E-Catalog Systems in B2B Procurement,” Communications of ACM Vol. 43, No. 5, May 2000, p.93-p.100.
[7] Basu, C., Hirsh, H. and Cohen, W., “Recommendation as Classification： Using Social and Content-Based Information in Recommendation,” Proceedings of AAAI-98, 1998.
[8] Billsus, D. and Pazzani, M. J., “Learning Collaborative Information Filters,” Proc. 15th International Conf. on Machine Learning, 1998, p.46-p.54.
[9] Breese, J. S., Heckerman, D. and Kadie, C., “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, July 1998, p.43-p.52.
[10] Cetintemel, U., Franklin, M. J. and Giles, C. Lee, “Self-Adaptive User Profiles for Large-Scale Data Delivery,” Proc. 16th ICDE, San Diego, February 2000.
[11] Chen, M. S., Park, J. S. and Yu, P. S., “Data Mining for Path Traversal Patterns in a Web Environment,” In Proceedings of the 16th International Conference on Distributed Computing Systems, May 27-30 1996, p.385-p.392.
[12] Chen, M. S., Park, J. S. and Yu, P. S., “Efficient Data Mining for Path Traversal Patterns,” IEEE Trans. on Knowledge and Data Engineering, 10(2), March 1998, p.209-p.221.
[13] CommerceNET, “A Finding Technology for Web Catalogs： The Parametric Search,” CommerceNET Research Note #97-14, May 1, 1997.
[14] Cooley, R., Mobasher, B. and Srivastava, J., “Creating adaptive Web sites through usage-based clustering of URLs,” In IEEE Knowledge and Data Engineering Workshop (KDEX’’99), 1999.
[15] Cooley, R., Mobasher, B. and Srivastava, J., “Data Preparation for Mining World Wide Web Browsing Patterns,” Knowledge and Information Systems Vol. 1, No.1, 1999, p5-p.32.
[16] Cooley, R., Mobasher, B. and Srivastava, J., “Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns,” Technical Report TR 97-021, Dept. of Computer Science, Univ. of Minnesota, Minneapolis, USA, June 1997.
[17] Cooley, R., Mobasher, B. and Srivastava, J., “Web Mining： Information and Pattern Discovery on the World Wide Web,” In Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’’97), November 1997.
[18] Datta, A., Dutta, K., VanderMeer, D., Ramamritham, K. and Navathe, S. B., “An Architecture to Support Scalable Online Personalization on the Web,” In Proceedings of the 2th ACM Conference on Electronic Commerce (EC’00), October 17-20, 2000, Minneapolis, Minnesota.
[19] Datta, A., Dutta, K., VanderMeer, D., Ramamritham, K. and Navathe, S. B., “An Architecture to Support Scalable Online Personalization on the Web,” VLDB Journal, Vol.10, No.1, 2001, p.104-p.117.
[20] Fasulo, D., “An Analysis of Recent Work on Clustering Algorithms,” http:// www.cs.washington.edu/homes/dfasulo/clustering.ps, 1999.
[21] Good, N., Schafer, J. B., Konstan, J. A., Boechers, A., Sarwar, B., Herlocker, J. and Riedl, J., “Combining Collaborative Filtering with Personal Agent for Better Recommendations,” In Proceedings of the Sixteenth National Conference on Artificial Intelligence, 1999.
[22] Henzinger, M. R., “HyperLink Analysis for the Web,” IEEE Internet Computing, Vol.5 No.1, January/February 2001, p.45-p.50.
[23] Jaczynski, M. and Trousse, B., “Broadway：A Case-Based System for Cooperative Information Browsing on the World-Wide-Web,” Collaboration between Human and Artificial Societies, 1999, p.264-p.283.
[24] Jaideep, S., Cooley, R., Deshpande, M. and Tan, P. N., “Web Usage Mining：Discovery and Applications of Usage Patterns from Web Data,” SIGKDD Explorations Vol.1 No.2, 2000, p.12-p.23..
[25] Jung, J., Kim, D., Lee, S. G., Wu, C. and Kim, K., “EE-Cat： Extended Electronic Catalog for Dynamic and Flexible Electronic Commerce,” 1999.
[26] Kappel, G., Retschitzegger, W. and Schwinger, W., “Modeling Customizable Web Applications A Requirement’’s Perspective, “Kyoto International Conference on Digital Libraries, 2000, p.387.
[27] Keller, A. M., “Smart Catalog and Virtual Catalogs,” Workshop on Electronic Commerce following CIKM, December 1994.
[28] Kim, J. G., Lee, E. S. K., “Intelligent Information Recommend System on the Internet,” IEEE, 1999.
[29] Kohavi, R., “Mining E-Commerce Data：The Good, the Bad, and the Ugly,” KDD 2001’s Industrial Track, 2001.
[30] Konstan, J. and Riedl, J., “Research Resources for Recommender Systems,”
[31] Kosala, R., Blockeel, H., “Web Mining Research： A Survey,” ACM SIGKDD, Volume 2, Issue 1, July 2000.
[32] Lai, H. C. and Yang, T. C., “A System Architecture of Intelligent-Guided Browsing on the Web,” IEEE, Proc. 31 Annual Hawaii International Conference on System Sciences, 1998.
[33] Lai, H. C. and Yang, T. C., “Agent Technology for Web Site Browsing and Navigation,”
[34] Lukacs, G., “Decision Support under Imperfections in Electronic Commerce,” IEEE Computer Society Press, 2000.
[35] Mobasher, B., Cooley, R. and Srivastava, J., “Creating Adaptive Web Sites Through Usage-Based Clustering of URLs,” .In IEEE Knowledge and Data Engineering Workshop (KDEX’’99), 2000.
[36] Mobasher, B., Cooley, R. and Srivastava, J., ”Automatic Personalization Based on Web Usage Mining,” In Communications of the ACM, Vol.43, No.8, 20
00,p.142-p.151.
[37] Morrision, J. B., Pirolli, P. and Card, S. K., “A Taxonomic Analysis of What World Wide Web Activities Significantly Impact People’s Decisions and Actions,” Xerox PARC Tech. Report UIR-R-2000-17, 1998.
[38] Perkowitz M. and Etzioni, O., “Adaptive Web Sites,” In Communications of the ACM Vol. 43, No. 8, Agust 2000.
[39] Perkowitz, M. and Etzioni, O., “Towards Adaptive Web Sites： Conceptual Framework and Case Study,” Computer Networks (Amsterdam, Netherlands: 1999), Vol.31, No.11-No.16, p.1245-p.1258.
[40] Pirolli, P., Pitkow, J. and Rao, R., “Silk from a Sow’s Ear： Extracting Usable Structures from the Web,” submitted to the Conference on Human Factors in Computing Systems, CHI-96, 1996.
[41] Pretschner, A., Gauch, S., “Ontology Based Personalized Search,” ICTAI, 1999, p.391-p.398.
[42] Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J., “GroupLens： An Open Architecture for Collaborative Filtering of NetNews,” Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work,Chapel Hill, NC, 1994, Pages 175-186.
[43] Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B. and Riedl, J., “Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System,” In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW), 1998.
[44] Sarwar, B.M., Karypis, G.., Konstan, J. and Riedl, J., “Analysis of Recommendation Algorithms for E-Commerce,” In Proceedings of ACM E-Commerce, 2000.
[45] Schafer, J. B., Konstan, J. and Riedl, J., “Recommender Systems in E-Commerce,” ACM Conference on Electronic Commerce, 1999, p.158-p.166.
[46] Schafer, J. B., Konstan, J. and Riedl, J., “E-Commerce Recommendation Application,” Data Mining and Knowledge Discovery, Vol.5 No.1/2, 2001, p.115-p.153.
[47] Segev, A., Wan, D. and Beam, C., “Designing Electronic Catalogs for Business Value： Result of the CommerceNet Pilot,” CMIT Working Paper 95-WP-1005, UC Berkeley, 1995.
[48] Shahabi, C., Kashani, F. B., Farugue, J. and Faisal, A., “Feature Matrices： A Model for Efficient and Anonymous Mining of Web Navigations,” http://www.usc.edu/dept/cs/technical_reports.html, 2000.
[49] Shardanand, U. and Maes, P., “Social Information Filtering for Music Recommendation,” S.M. Thesis, Program in Media Arts and Sciences, Massachusetts Institute of Technology, 1994.
[50] Shardanand, U. and Maes, P., “Social Information Filtering： Algorithms for Automationg “Word of Mouth”,” Proceedings of CHI’’95 Conference on Human Factors in Computing Systems, ACM Press, 1995.
[51] Shim, S. S.Y., Pendyala, V. S., Sundaram, M. and Gao, J. X., “Business-to-Business E-Commerce Frameworks,” IEEE, 2000.
[52] Singh, L., Scheuermann, P. and Chen, B., “Generating Association Rules from Semi-Structured Documents Using an Extended Concept Hierarchy,” In Proceedings of the 6th International Conference on Information and Knowledge Management. Las Vegas, Nevada, USA, 1997, pp. 193-200.
[53] Srivastava J., Cooley, R., Deshpande, M., Tan, P. N., “Web Usage Mining： Discovery and Applications of Usage Patterns from Web Data,” SIGKDD Explorations, Vol.1, No.2, 2000, p.12-p.23.
[54] Steiger, P. and Stolze, M., “Effective Product Selection in Electronic Catalogs,” CHI 97 Electronic Publication： Late-Breaking/Short Talks, 1997.
[55] Strehl, A., Ghosh, J. and Mooney, R., “Impact of Similarity Measures on Web-page Clustering,” AAAAI-2000： Workshop of Artificial Intelligence for Web Search, July 2000.
[56] Thuraisingham, B., “Web Data Management and Electronic Commerce,” CRC Press, 2000.
[57] Troussse, B., “Evaluation of the Prediction Capability of a User Behaviour Mining Approach for Adaptive Web Sites,” In Proceedings of the 6th RIAO Conference - Content-Based Multimedia Information Access, Paris, France, april, 2000.
[58] Wong, W. T. and Keller, A. M., “Developing an Internet Presence with On-line Electronic Catalogs,” CMIT Working Paper 95-WP-1005, UC Berkeley, October 9, 1994.
[59] 林裕展,設計與實作以可延伸性標示語言為基礎之電子目錄,國立交通大學資管所碩士論文,1998.
[60] 陳雪美譯, 電子商務概論,和碩科技文化有限公司, 1999.
譯自：Kalakota, R. and Whinston, A. B., “Frontiers of Electronic Commerce.”
[61] 魏志仲,電子商務聖經：電子商務概念, 第三波, 2000.

指導教授

林熙禎(Shi-Jen Lin)

審核日期

2002-7-5

推文