臺灣博碩士論文加值系統

在目前網路技術與設備普及的時代，網際網路儼然成為人們取得生活資訊的重要管道。許多網路活動也隨著這股潮流興起，爆炸性充斥著各種網路資訊，亟需多層面應用的資訊推薦服務來改善資訊過載問題。根據學者對推薦機制的論證，不同的推薦機制各有其推薦效益和限制;而結合知識導向的混合式推薦機制，受到學者的關注，並被期待能解決線上未統一資料格式資料之資訊推薦問題並推薦提升推薦成效。以現今網路部落格現象為例，人們樂於分享並閱讀網友們分享的文字知識資料，然而線上部落格資訊內容眾多且格式標準不一，難以在有限時間內達到個人搜尋目標。因此，本研究綜合相關提出以知識導向為基礎，並結合內容導向與協同過濾的混合式推薦機制方法，期待能改善現有存在於線上部落格簡易的關鍵字搜尋推薦機制的效能，並以實驗方法，提出效能最佳的推薦機制。
本研究建立一個互動式實驗網站平台，應用網路探勘技術進行實驗研究，藉由使用者對旅遊資訊的部落格之瀏覽紀錄，瞭解使用者的偏好以進行實驗性個人化知識推薦，並藉由使用者的主觀感受回饋來分析與評估各實驗結果的推薦績效。經實驗分析結果顯示，使用者對以知識混合推薦機制評估上顯示有在統計上的顯著喜好。文末，本論文針對結論和未來研究方向均有做討論和分析。本研究可視為對線上資訊推薦系統的改善和相關研究的基礎。

As the network technology and equipments to the popularization of the Internet, it becomes a major information and knowledge source for people. However, there is a need to improve the information overload problem. Take the recommendation systems as the cases, the adopted recommendation mechanisms can be categorized as content-oriented, collaborative and knowledge-based filtering ones. Each mechanism has its pros and cons. The knowledge-oriented hybrid recommendation mechanisms is denoted as the most efficient one in assisting online information search. In this study, the case of online blog is taken for further examination due to the fact that it has become a phenomenon in the Internet. People take delight in surfing blogs. However, online blogs fall in unstandardized formats. It makes the recommendation system need to solve the problems of natural language usage problem. Therefore, the idea of adopting knowledge-based information recommendation mechanism combined with web mining technology is set to be tested experimentally in this study.
This thesis aims at investigating the notion of recommendation system from the literature and test the effectiveness of hybrid recommendation mechanism by a user-centered method. The experimental method is adopted. Results indicate that the knowledge-based hybrid recommendation mechanism with the combinations of content-based and collaborative filtering mechanisms outperform the others statistically significant. Results and future research opportunities are discussed and given. This study is served as the basis for the improvements of recommendation systems and the related areas.

致謝詞 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 前言 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究操作假設 4
第四節 研究流程 5
第二章 文獻探討 6
第ㄧ節 部落格 6
ㄧ、部落格的意義與原因 6
二、管理部落格的方法 7
第二節 推薦系統(Recommendation System) 7
ㄧ、推薦系統的定義 7
二、內容導向(Content-based)推薦機制 8
三、協同過濾(Collaborative Filtering)推薦機制 9
四、知識導向(Knowledge-based)推薦機制 10
五、混合式(Hybrid)推薦機制 11
六、推薦系統的評估方法 14
第三節 個人化網路推薦服務 18
第四節 網路探勘(Web Mining) 20
一、網路探勘的定義 20
二、網路使用探勘的過程 21
三、網路使用探勘與個人化資訊推薦 22
第三章 研究方法 23
第一節 知識網頁前處理 24
ㄧ、旅遊知識庫來源 24
二、旅遊知識庫架構 25
三、部落格知識網頁來源 27
第三節 紀錄使用者行為 29
第四節 知識推薦後處理 30
第五節 評估推薦結果 34
第四章 系統實作與實驗結果 35
第一節 建置實驗網站 35
一、實作環境 35
二、資料庫設計 35
三、個人化知識混合推薦實驗系統架構 36
四、個人化知識混合推薦實驗網站架構 37
第二節 個人化知識推薦策略實驗結果 48
第三節 驗證與分析實驗結果 52
ㄧ、實驗ㄧ：知識搜尋方式 53
二、實驗二：知識混合推薦方式 55
三、實驗結果的分析與討論 57
第五章結論與未來研究方向 59
第一節 研究結論 59
第二節 研究貢獻 60
第四節 未來研究方向 61
參考文獻 62

一、中文資料
1. 丁一賢、陳牧言，2003，資料探勘，臺北市：滄海書局。
2. 于健、康代育，2007，「消費者渡假生活型態對旅遊刊物選擇影響之研究」，環境與管理研究，第八卷第ㄧ期：43-59頁。
3. 王佳鳳、黃正聰，2006，「運用資料採礦於台灣觀光資訊入口網站瀏覽行為分析」，網路生活學術研討會，台北：國立台北大學。
4. 王婷儀、民95，以虛擬經驗為基礎之個人化產品推薦系統建置，國立臺北科技大學商業自動化與管理研究所碩士論文。
5. 林惠玲、陳正倉，2006，統計學：方法與應用，臺北市：雙葉書廊。
6. 林熙禎、許益誠，2004，「以網站探勘技術為基礎之電子目錄上推薦服務之研究」，資管評論，第十三期：59-73頁。
7. 陳禹辰、民95，旅遊部落格對旅遊地點印象之影響，私立東吳大學企業管理學系碩士論文。
8. 陳振東、戴偉勝，2002，「網際網路環境中個人化資訊推薦系統實作之研究」，資訊管理學報，第九卷，第一期：21-37頁。
9. 楊亨利、林青峰，2006，「全球資訊網中網頁-動作使用路徑的資料挖掘」，資訊管理學報，第十三卷，第四期：27-52頁。
10. 賴宏仁、民88，電子報個人化新聞推薦方法之研究，國立中山大學資訊管理系博士論文。
11. 蘇俊斌、民93，應用網站探勘技術於網友瀏覽行為分析-以內容服務網站為例，國立臺灣大學資訊管理學研究所碩士論文。

二、英文資料
1. Adomavicius, G. and Tuzbilin, A. “Using Data Mining Methods to Build Customer Profiles,” IEEE Computer (34:2), 2001, pp. 74-82.
2. Adomavicius, G. and Tuzbilin, A. “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Transactions on Knowledge and Data Engineering (17:6), 2005, pp. 734-749.
3. Balabanovic, M. and Shoham, Y. “Fab: Content-based, Collaborative Recommendation,” Communications of the ACM (40:3), 1997, pp. 66-72.
4. Baraglia, R. and Silvestri, F. “Dynamic Personalization of Web Sites Without User Intervention,” Communications of the ACM (50:2), 2007, pp. 63-67.
5. Bausch, P., Haughey, M., and Hourihan, M. We Blog: Publishing Online with Weblogs, Indianapolis, Wiley, 2002.
6. Brooks, C.H. and Montanez, N.“An Analysis of the Effectiveness of Tagging in Blogs,” Processing of the 2005 AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, Menlo Park, California, 2005.
7. Burke, R. “Integrating Knowledge-based and Collaborative-filtering Recommender Systems,” Processing of the Workshop on AI and Electronic Commerce (AAAI 99),1999.
8. Burke, Robin “Hybrid Recommender Systems: Survey and Experiments,” User Modeling and User-Adapted Interaction (12), 2002, pp. 331-370.
9. Cao, Y. and Li, Y. “An Intelligent Fuzzy-based Recommendation System for Consumer Electronic Products,” Expert Systems with Applications (33), 2007, pp.230-240.
10. Cayzer, S. “Semantic Blogging and Decentralized Knowledge Management,” Communications of the ACM (47:12), 2004, pp. 47-52.
11. Cheung, K. W., Kwok, J. T., Law, M.H., and Tsui, K.C. “Mining Customer Product Ratings for Personalized Marketing,” Decision Support Systems (35), 2003, pp. 231-243.
12. Clyde, L. A. Weblogs and Libraries, Oxford:Chandos Publishing, 2004.
13. Corby, O., Dieng-Kuntz, R., Faron-Zucker, C., and Gandon, F. “Searching the Semantic Web: Approximate Query Processing Based on Ontologies,”IEEE Intelligent Systems (21:1), 2006, pp. 20-27.
14. Felfernig, A., Friedrich, D., Jannach, D., and Zanker, M. “An Environment for the Development of Knowledge-based Recommender Applications,” International Journal of Electronic Commerce (11:2), 2006, pp. 11-34.
15. Hayes, C., Avesani, P., and Veeramachaneni, S. “An Analysis of the Use of Tags in a Blog Recommender System,” Processing of the 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, 2007, pp. 2772-2777.
16. Herlocker, J., Konstan, J., Terveen, L., and Riedl, J.“Evaluating Collaborative Filtering Recommender Systems,”ACM Transactions on Information Systems(22:1), 2004, pp. 5-53.
17. Herlocker, J., Konstan, J. A., and Riedl, J. “Explaining Collaborative Filtering Recommendations,” Processing of ACM 2000 Conference on Computer Supported Cooperative Work (CSCW ’00), 2000, pp. 241-250.
18. Huang, Z., Ng, J., Cheung, D.W., Ng, M.K., and Ching, W. “A Cube Model for Web Access Sessions and Cluster Analysis,” Processing of the 3rd International Workshop on Mining Web Data (WEBKDD 2001),San Francisco, 2001, pp. 47-57.
19. Hung, L.P. “A Personalized Recommendation System Based on Product Taxonomy for One-to-one Marketing Online,” Expert Systems with Applications (29), 2005, pp. 383-392.
20. Kim, J. K., Cho, Y. H., Kim, W. J., Kim, J. R., and Suh, J. H. “A Personalized Recommendation Procedure for Internet Shopping Support,” Electronic Commerce Research and Applications (1), 2002, pp. 301-313.
21. Kim, T. K. and Yang, S. B. “An Effective Recommendation Algorithm for Clustering-Based Recommender Systems,”Lecture Notes in Computer Science (3809), 2005, pp. 1150-1153.
22. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of the ACM (40:3), 1997, pp. 77-87.
23. Krulwich, B. and Burkey, C. “The InfoFinder Agent Learning User Interests through HeuristicPhrase Extraction,” IEEE Expert Magazine (12:5), 1997, pp. 22-27.
24. Lee, J.H. and Shiu, W.K. “An Adaptive Website System to Improve Efficiency with Web Mining Techniques,” Advanced Engineering Informatics (18), 2004, pp. 129-142.
25. Lee, J., Podlaseck, M., Schonberg, E., and Hoch, R. “Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising,” Data Mining and Knowledge Discovery (5), 2001, pp. 59-84.
26. Lee, W.P. “Applying Domain Knowledge and Social Information to Product Analysis and Recommendations: an Agent-based Decision Support System,” Expert Systems (21:3), 2004, pp. 138-148.
27. Lee, C.H., Kim, Y.H., and Rhee, P.K. “Web Personalization Expert with Combining Collaborative Filtering and Association Rule Mining Technique,” Expert Systems with Applications (21), 2001, pp. 131-137.
28. Li, Q. and Zhou, M. “Research and Design of an Efficient Collaborative Filtering Predication Algorithm,” Processing of the IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'2003), 2003, pp. 171-174.
29. Liang, T.P., Lai, H.J., and Ku, Y.C. “Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings,” Journal of Management Information Systems (23:3), 2006, pp. 45-70.
30. Liang, T. P., Yang, Y.F., Chen, D.N.,and Ku, Y. C. “A semantic-expansion approach to personalized knowledge recommendation,” Decision Support Systems (45:3), 2008, pp. 401-412.
31. Linden, G., Smith, B., and York, J. “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing(7:1), 2003, pp. 76-80.
32. Linoff, G. S. and Berry, M. J. A. Mining the Web: Transforming Customer Data into Customer Value, New York:John Wiley, 2001.
33. McNee, S. M.,Riedl, J., and Konstan, J. A. “Accurate Is Not Always Good: How Accuracy Metrics Have Hurt Recommender Systems,”Extended Abstracts on Human factors in Computing Systems (CHI 2006), 2006, pp. 1097–1101.
34. Mulvenna, M. D., Anand, S. S., and Buchner, A. G. “Personalization on the Net Using Web Mining,” Communications of the ACM (43:8), 2000, pp. 122-125.
35. Nardi, B. A., Schiano, D. J., Gumbrecht, M., and Seartz, L. “Why We Blog,” Communications of the ACM (47:12), 2004, pp. 41-46.
36. Ohmukai, I., Numa, K., and Takeda, H. “Egocentric Search Method for Authoring Support in Semantic Weblog,” Processing of Workshop on Knowledge Markup and Semantic Annotation(Semannot2003), 2003.
37. Pan, B., MacLaurin, T., and Crotts, J. C. “Travel Blogs and the Implications for Destination Marketing,”Journal of Travel Research (4:1), 2007, pp. 35-45.
38. Pazzani, M. J. “A Framework for Collaborative, Content-Based and Demographic Filtering,”Artificial Intelligence Review (13:5-6), 1999, pp. 393-408.
39. Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P. and Riedl, J. “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Processing of ACM CSCW’94 Conference on Computer-Supported Cooperative Work, 1994, pp. 175-186.
40. Saracevic, T., Kantor. P., Chamis, A. Y., and Trivison, D. “A Study of Information Seeking and Retrieving. I. Background and Methodology,” Journal of the American Society for Information Science(39:3), 1998, pp. 161-176.
41. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. “Analysis of Recommendation Algorithms for E-Commerce,” Processing of the 2nd ACM Conference on Electronic Commerce, 2000.
42. Schafer, J. B., Konstan, J. A., and Riedl, J. “E-Commerce Recommendation Applications,” Data Mining and Knowledge Discovery (5:1-2), 2000, pp. 115-153.
43. Schiel, U., Souza Baptista, C., Jesus Maia, A. C., and Andrade, F. G. “SEI-Tur: A system based in Composed Web-Service Discovery to Support the Creation of Trip Plans,” Processing ofthe First International Conference on the Digital Society (ICDS 2007), 2007.
44. Shih, T. K., Chiu, C.-F., Hsu, H.-H., and Lin, F. “An Integrated Framework for Recommendation Systems in E-commerce,” Industrial Management &; Data Systems (102:8), 2002, pp. 417-431.
45. Srivastava, J., Cooley, R., Deshpande, M., and Tan, P.N. “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data,” ACM SIGKDD Explorations (1:2), 2000, pp. 12-23.
46. Sugumaran, V., and Storey, V. C. “Ontologies for Conceptual Modeling: Their Creation, Use, and Management,”Data and Knowledge Engineering (42:3), 2002, pp. 251-271.
47. Terveen, L., Hill, W., Amento, B., McDonald, D., and Creter, J. “PHOAKS: A System for Sharing Recommendations,”Communications of the ACM (40:3), 1997, pp. 59-62.
48. Thevenot, G. “Blogging as a Social Media,”Tourism and Hospitality Research (7), 2007, pp. 287-289.
49. Wang, H.C. and Chang, Y.L. “PKR: A Personalized Knowledge Recommendation System for Virtual Research Communities,” The Journal of Computer Information Systems (48:1), 2007, pp. 31-41.
50. Weng, S.S. and Liu, M.J. “Personalized Product Recommendation in E-Commerce,” Processing of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004.
51. Yang, W., Wang, Z., and You, M. “An Improved Collaborative Filtering Method for Recommendations’ Generation,” Processing of IEEE International Conference on Systems, Man and Cybernetics (5), 2004, pp. 4135-4139.
52. Ying, Y., Feinberg, F., and Wedel, M. “Leveraging Missing Ratings to Improve Online Recommendation Systems,” Journal of Marketing Research (43:3), 2006, pp. 355-365.
53. Yao, Y. Y. “Measuring Retrieval Effectiveness Based on User Preference of Documents,”Journal of the American Society for Information Science and Technology (46:2), 1995, pp. 133-145.
54. Yu, P. S. “Data Mining and Personalization Technologies,” Processing of the 6th International Conference on Database System for Advanced Applications, 1999.
55. Zhang, Y. and Jiao, J. “An Associative Classification-based Recommendation System for Personalization in B2C E-commerce Applications,” Expert Systems with Applications (33), 2007, pp. 357-367.