網際網路(Internet)與全球資訊網(World Wide Web；WWW)技術的蓬勃發展，使得各式各樣的資訊都能經由網路取得，此外，數位電子化技術的成熟也促使電子圖書、e-journal等網際網路資源快速增加。藉由網際網路與數位資訊的推波助瀾，使用者可以不受時空限制地透過瀏覽器查詢數位化後的各類型文件資源。網際網路所提供的豐富資訊雖然讓使用者擁有更多的選擇機會，卻也同時產生「資訊過載」(information overload)的困擾。這使得從網際網路中找到真正相關的資訊變成一件費時且沒有效率的工作。目前電子化文件的查詢方法一般為透過搜尋引擎的關鍵字查詢，搜尋引擎的關鍵字搜尋結果卻面臨三個問題：第一、搜尋的結果常含有過多未精練的資訊，使用者必須一一過濾以取得符合需要的資訊；第二、使用者必須先很明確的知道想要搜尋資料的方向及關鍵字，才能有效的利用搜尋功能；第三、搜尋的方式在於只要搜尋字串及條件符合，即會將結果提供給使用者，因此不一定能符合使用者的需求與興趣；此外，在強調知識管理的今天，我們對於知識的取得，卻往往只強調顯性的知識，而忽略了隱性知識最重要來源「人」的溝通價值。
爲了提供更有效益的顯隱性知識以及改善資訊與知識搜尋結果未適性化的問題，本研究提出一個人化顯隱性知識推薦方法來提供個人化資訊推薦服務，以滿足每位使用者對於顯性知識文件與隱性知識同好的需求。其推薦方法的設計主要是結合協同過濾與內容過濾兩者，以協同過濾考慮群組興趣的概念來過濾文件品質並實施隱性知識的推薦；再以內容過濾來緩和協同過濾評比稀疏及cold start的問題，兩種資訊過濾方式併行，避免單一方式所衍生的問題。同時本研究實際建置一「個人化推薦模型」(Personalized Recommendation Model；PRM)，以E-journal paper推薦為範例進行一實驗研究，並以不同的評估指標來分析與評估PRM的推薦績效，結果顯示不論在MAE值、precision值、recall值與F-measure值的表現上，都有明顯提昇的推薦效果，足以驗證本研究所提出的個人化推薦方法之效果。

As the rapid development of Internet and World Wide Web (WWW), the Internet and WWW have become a major information and knowledge source for people. The progress of the electronic technology has also caused the style of conventional paper-publishing documents changed and toward the electronic style. By the Internet, people could use browsers to search various digitalized documents without spatial and temporal constraints. The Internet provides a powerful disseminative ability for users to acquire information more efficient and fast and provides opportunities for information findings. However, the exponentially increasing information provided by the Internet causes the problem of information overload and the rapid growth of the number of e-documents has also made information discovery from the Internet a time-consuming and inefficient task.
In general, we search proper e-documents through search engine service. Search engine allows user to retrieve relevant papers by entering keywords. Although a search engine provides convenient documents search, there are still three serious problems that affect search engine service: First, users have to check many returned papers in order to locate the desired information. Second, users must know exactly what keywords will lead them to the right direction. Third, we usually focus on the recommendation of explicit knowledge, but ignore the importance of tacit knowledge recommendation. And those problems become worse along with the information overload.
To overcome the problems mentioned above, we propose a personalized e-documents recommendation method to provide personalized information service. Our method combines Content Filtering and Collaborative Filtering and consists of two parts: tacit knowledge recommendation and explicit knowledge recommendation. This method can automatically extracts the interests of a user according to his past ratings and actions. After we find out the interests of a user, we can recommend information that is related to the interests of the user. Finally, we implement a Personalized Recommendation Model (PRM) and use MAE, precision, recall and F-measure to evaluate the performance of PRM. And the results show that PRM performs well in those measures. PRM can not only create the specialty, but also improve the satisfaction of e-documents information service

張瀚仁(民89)。個人化技術對虛擬社群發展之影響，國立政治大學資訊管理研究所碩士論文。

Allen, C. (1999). Personalization vs. customization. Available at: http://www.clickz.com/mkt/precis_mkt/article.php/814811
Allen, C., Kania D., & Yaeckel, B. (1998). Internet World Guide to One-To-One Web Marketing, John Wiley & Sons.
Balabanovic, M., & Shoham, Y. (1997). Fab: Content-Based, Collaborative Recommendation. Communications of the ACM, 40(3), 66-72.
Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. Proceedings of the 15th National Conference on Artificial Intelligence, 714–720.
Bollacker, K., Lawrence S., & Giles, C.L. (1999). A system for automatic personalized tracking of scientific literature on the web. Digital Libraries The Fourth ACM Conference on Digital Libraries, ACM Press, New York, 105–113.
Bowman, C. M., Danzig, P. B., Manber, U., & Schwartz, M. F. (1994). Scalable Internet resource discovery research problems and approaches. Communications of the ACM, 37(8), 98-107.
Breese, J. S., Heckerman, D., & Kadie, C. (1998).Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence , 43-52.
Chau, M., Zeng, D., Chen, H., Huang, M., & Hendriawan, D. (2002) (in press). Design and evaluation of a multi-agent collaborative web mining system. Decision Support Systems.
Chen, C. C., Chen, M.C., & Sun, Y. (2002). PVA：A Self-Adaptive Personal View Agent. Journal of Intelligent Information Systems,18, 173-194
Chen, H., Chung, Y.M., Ramsey, M., & Yang, C.C. (1998). An Intelligent personal spider (agent) for dynamic internet/intranet searching. Decision Support Systems, 23(1),41-58.
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M. (1999). Combining Content based and Collaborative Filters in an Online Newspaper. Proceedings of the ACM SIGIR '99 Workshop on Recommender Systems: Algorithms and Evaluation. University of California, Berkeley.
Claypool, M., Brown, D., Le, P., & Waseda M. (2001). Inferring User Interests. IEEE Internet Computing, 5(6),32-39.
Connor, O.M., & Herlocker, J. (1999). Clustering Items for Collaborative Filtering. Proceedings of the Workshop on Recommender System: Algorithms and Evaluation.
Cosley, D., Lawrence, S., & Pennock, D.M. (2002). REFEREE: An open framework for practical testing of recommender systems using ResearchIndex. Proceedings of the 28th International Conference on Very Large Databases.
Dean, R. (1998). Personalizing your web site. Available at http://builder.cnet.com/webbuilding/pages/Business/Personal/
Dickson, G.W., Senn, A. J., & Chervany, N. L. (1977). Research in management information system: The Minnesota experiments. Management Science, 23(9), 913-924.
Edmunds, A., & Morris A. (2000). The problem of information overload in business organizations：a review of the literature. International Journal of Information Management, 20, 17-28.
Goldberg, D., Nichols, D., Oki, M.B., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of ACM, 35(12), 61-70
Goldberg, K., Roeder, T., Gupta, D., & Perkins, K.(2001). Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval, 4(2), 133-151.
Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B. M., Herlocker, J. L., & Riedl, J. (1999). Combining collaborative filtering with personal agents for better recommendations. Proceedings of the 16th National Conference on Artificial Intelligence, 439–446.
Herlocker, J., Konstan, J. A., Borchers, A., & Riedl, J. (1999).An Algorithmic Framework for Performing Collaborative Filtering. Proceedings of SIGIR 99, Berkeley, 230-237.
Herlocker, J. (2000). Understanding and Improving Automated Collaborative Filtering Systems. Ph.D.Thesis, Computer Science Dept., University of Minnesota.
Hill, W., Stead, L., Rosenstein, M., & Furnas, G.(1995) Recommending and evaluating choices in a virtual community of use. Proceedings of CHI 95, Denver, 194-201.
Kautz, H., Selman, B., & Shah, M. (1997). Referral Web: Combining Social Networks and Collaborative Filtering. Communications of the ACM, 40(3), 63-65.
Kolda, T.G., & O'Leary, D.P. (1998). A Semidiscrete Matrix Decomposition for Latent Semantic Indexing in Information Retrieval. ACM Transactions on Information Systems, 16(4),322-346.
Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM, 40(3), 77-87.
Korfhage, R. R. (1997). Information Storage and Retrieval. New York, NY：Wiley Computer Publishing.
Krulwich, B., & Burkey, C. (1997). The InfoFinder agent: Learning user interests through heuristic phrase extraction. IEEE Intelligent Systems Journal (Expert), 12(5), 22-27.
Karypis, G. (2000). Evaluation of Item-Based Top-N Recommendation Algorithms. Technical Report CS-TR-00-46, Computer Science Dept., University of Minnesota.
Lang K. (1995). NewsWeeder: Learning to filter netnews. Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA.331-339.
Lawrence, R. D., Almasi, G. S., Kotlyar, V., Viveros, M. S., & Duri, S. S. (2001). Personalization of supermarket product recommendations. Data Mining and Knowledge Discovery, 5(1–2), 11–32.
Lieberman, H. (1995). Letizia: An agent that assists web browsing. Proceedings of the International Joint Conference on Artificial Intelligence, Montreal.
Lin, W., Alvarez, S.A., & Ruiz, C. (2002). Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6,83–105.
Middleton, S.E., De Roure, D. C., & Shadbolt, N.R. (2001). Capturing Knowledge of User Preferences: ontologies on recommender systems. Proceedings of the 1st International Conference on Knowledge Capture.
Mittal, B., & Lassar, W. (1996). The role of personalization in service encounters. Journal of Retailing, 14(1), 95-109.
Mobasher, B., Cooley, R., & Srivastava, J. (2000a). Automatic personalization based on web usage mining. Communications of the ACM, 43(8), 142–151.
Mobasher, B., Dai, H., Luo, T., Nakagawa, M., Sun, Y., & Wiltshire, J. (2000b). Discovery of aggregate usage profiles for web personalization. ACM WebKDD Web Mining for E-Commerce Workshop.
Mobasher, B., Dai, H., Nakagawa, M., & Luo, T. (2002). Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery, 6,61-82.
Morita, J., & Shinoda, Y. (1994). Information filtering based on user behavior analysis and best match text retrieval. Proceeding of the 7th annual ACM-SIGIR conference on research and development in information retrieval, New York：ACM Press,272-281.
Nonaka, I., & Takeuchi, H. (1994). A dynamic theory of organization knowledge creation. Organizational Science, 5, 14-37.
Oppenheim, C. (1997). Managers’ use and handling of information. International Journal of Information Management, 17(4), 246.
Pazzani, M.J. (1999). A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Reivew, Available at: http://www.ics.uci.edu/~pazzani/Publications/AIREVIEW.pdf
Pennock, D.M., Horvitz, E., Lawrence, S., & Giles, C.L. (2000). Collaborative filtering by personality diagnosis：A hybrid memory-and model-based approach. Proceedings of the 16th of Conference on Uncertainty in Artificial Intelligence, 473-480.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Ried, J. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of the ACM Conference on Computer Supported Cooperative Work , 175-186.
Resnick, P., & Varian, R.H. (1997). Recommender systems. Communication of ACM, 40(3), 56-58.
Rucker, J., & Polanco, J.M. (1997). Siteseer: Personalized Navigation for the Web. Communications of ACM, 40(3), 73-75.
Sakagami, H., & Kamba, T. (1997). Learning Personal Preferences on Online Newspaper Articles from User Behaviors. Computer Networks and ISDN Systems, 29,1447-1455.
Salton, G. (1989).Automatic Text Processing：The Transformation, Analysis, and Retrieval of Information by Computer. Reading, MA：Addison-Wesley.
Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. (2000a). Analysis of Recommendation Algorithms for E-Commerce. Proceedings of the ACM EC'00 Conference, Minneapolis, MN, 158-167.
Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. (2000b). Application of Dimensionality Reduction in Recommender System--A Case Study. ACM WebKDD 2000 Web Mining for E-Commerce Workshop.
Sarwar, B.M., Karypis, G., Konstan, J.A., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithm. Proceedings of the 10th International World Wide Web Conference, 285–295.
Schein A. I., Popescul A., Ungar L. H., & Pennock. D. M.(2002). Methods and metrics for cold-start recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
Scott, J.E. (1998). Organizational knowledge and the Intranet. Decision Support System, 23, 3-17.
Shenk, D. (1997). Data smog：Surviving the information age. London：Abacus, 31.
Shardanand, U., & Maes, P. (1995). Social information filtering: algorithms for automating "word of mouth". Proceedings of the Conference on Human Factors in Computing Systems , Denver, CO, ACM, 210-217.
Terveen, L., Hill, W., Amento, B., McDonald, D., & Creter, J. (1997). PHOAKS: A System for Sharing Recommendations. Communications of ACM, 40(3), 59-62.
Treese, G. W., & Stewart, L. C. (1998). Designing Systems for Internet Commerce. Addison Wesley Longman.
Vucetic, S., & Obradovic, Z. (2000). A regression-based approach for scaling-up personalized recommender systems in e-commerce. ACM WebKDD 2000 Web Mining for E-Commerce Workshop.
Watson A., & Sasse M.A. (1998). Measuring Perceived quality of Speech and Video in Multimedia Conference Application . Proceedings of ACM Multimedia Conference, 55-60.
Webb, G., Pazzani, M.J., & Billsus, D. (2001).Machine learning for user modeling. User Modeling and User-Adapted Interaction ,11,19-20.
Yan, T. W., & Garcia-Molina, H. (1999). The SIFT information dissemination system. ACM Transactions on Database Systems, 24(4), 529-565.
Yu, K., Xu, X., Ester, M., & Kriegel, H.P. (2001). Selecting relevant instances for efficient accurate collaborative filtering. Proceedings of the 10th nternational conference on Information and knowledge management, 239-246.