在現今數位化的時代中，圖書館經營的目的亦是要能夠滿足讀者的需求，而圖書館個人化服務已經成為是近年來重要的研究課題之一。本論文研究方向是以圖書館推薦系統為核心，探討其系統效能與使用者滿意度評估之研究。以讀者借閱資料為訓練來源，藉由隱性主題發掘技術及社會網路分析(SNA)的過程，以發掘讀者與讀者間借閱興趣的相似度，訂定讀者借閱資料的關聯性之權重高低，藉以得知讀者之最適性書籍推薦清單。
此外，透過系統效能評估結果與讀者滿意度問卷之調查，探討兩者評估結果中間所產生的落差，並找出如何主動發掘讀者的需求，以及提供讀者所需要的資訊。透過此項研究分析，來探討讀者使用圖書館之行為，不僅可提供圖書館經營管理者在決策館藏發展政策、圖書推薦，亦可以提供圖書館界在個人化的主題領域中更加廣泛、實用的服務效能。

In today's digital era, the library is also the purpose of business to be able to meet the needs of readers, and the library is the personal service has become an important research topic in recent years. Libraries in this research is based on recommendation system as the core of the system performance and user satisfaction assessment research. Readers training data to the source of the theme by hidden technology and social network analysis to explore (SNA) in the process, readers and readers to explore the similarity between the loan interest, information on Readers set the weights of the relevance of the level, readers to know the most adaptive book recommendations list.
In addition, through the performance assessment system and reader satisfaction survey questionnaire, the results of two assessments produced by the middle of the gap, and find out how to take the initiative to explore the needs of readers, and provide the information readers need. Through this study, to explore the behavior of readers use the library, not only for library managers in decision-making collection development policies, recommended books, libraries can be provided in individual subject areas of more extensive and practical service performance.

王石番等人（1991）。 傳播內容分析法--理論與實證。台北市，幼獅。

卜小蝶（1998）。 淺析個人化服務技術的發展趨勢對圖書館的影響。國立成功大學圖書館館刊，2，63-73。

劉崇汎（2008）。 智慧型個人化多媒體推薦系統之建置。Retrieved May 20, 2008, from http://www.datf.iis.sinica.edu.tw/Papers/2006datfpapers/35.pdf

謝安田（1998）。 企業研究方法論，台北：著者發行。

周寬怡（2003）。 以內容分析法獲取推薦系統中使用者profile之研究。國立成功大學資訊管理研究所碩士論文，未出版，台南市。

馮文正（2001）。 合作式網站推薦系統。國立交通大學資訊科學研究所碩士論文，未出版，新竹市。

陳昭宇（2005）。根基於自我組織特徵映射圖為基礎之最佳化演算法之推薦系統。國立中央大學資訊工程研究所碩士論文，未出版，桃園縣中壢市。

簡茂發（2000）。教育大辭書（五）。台北：文景書局，146-147。

邱迪凱（2007）。結合查詢擴展之學習元件個人化推薦系統。國立成功大學工程科學系碩士碩士論文，未出版，新竹市。

Adomavicius, G., and Tuzhilin, A. (2005). "Towards 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), 734-749.

Ansari, A., Essegaier, S., and Kohli, R. (2000). "Internet Recommendation Systems". Journal of Marketing Research, 37(3), 363-375.

Balabanovic, M. and Shoham, Y. (1997). Fab : Content-Based, Collaborative Recommendation. Communications of the ACM, 40(3), 66-72.

Billsus, D., C. A. Brunk, C. Evans, B. Gladish, and M. Pazzani (2002). Adaptive interfaces for ubiquitous web access. Commnications of the ACM, 45(5), 34-38.

Bower, J.W. (1970). "Content Analysis," in Philip Emmert and William D. Brooks (Eds.), Methods in Research in Communication, Boston, Mass.: Houghton Mifflin Co., 291-314.

Brown, S.M. (2000). Searching for Effective CRM. Enterprise System Journal, 5(8), 40-43.

Bruggeman, J. (2008). Social networks: An introduction, New York, Rout-ledge.

Chee, S. H. S., Han, J., and Wang, K. (2001). "RecTree: An Efficient Collaborative Filtering Method" , Proceedings of International Conference on Data Warehouse and Knowledge Discovery (DaWaK'01), 141-151.

David W. McDonald (2003). "Ubiquitous Recommendation Systems". Computer, 36(10), 111-112.

Gediminas Adomavicius, Alexander Tuzhilin (2005). Towards 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), 734-749.

Goldberg, D., Nichols, D., Oki, B. M., and Terry D. (1992). Using collaborative filtering to weave an information tapestry. Communication of the ACM, 35(12), 61-70.

Hanneman, Robert A. and Mark Riddle. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside. ( published in digital form at http://faculty.ucr.edu/~hanneman/ )

Herlocker, J. L., Konstan, J. A., and Riedl, J. (2000). Explaining Collaborative Filtering Recommendations. Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, 241-250.

Hernon, P., & Calvert, P. (2005). E-service quality in libraries: Exploring its features and dimensions. Library & Information Science Research, 27(3), 377-404.

Hill, W., L. Stead, M. Rosenstein, and G. Furnas (1995). Recommending and evaluating choices in a virtual community of use. In Proceedings of CHI’95. Addison-Wesley, New York, USA.

Huang, H. S. and Hsu, C. N. (2001). Smoothing of Recommenders' Ratings for Collaborative Filtering. Proceedings of the TAAI Conference on Artificial Intelligence and Applications.

Jian-hua Yeh, Meng-lun Wu.(2010). Recommendation Based on Latent Topics and Social Network Analysis. to appear in Proceedings of the 2nd International Conference on Computer and Engineering Applications(ICCEA 2010), Bali, Indonesia, 1, 209-213.

Konstan, J. A., Miller, B. N., and Maltz, D. (1997). GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3), 77- 87.

Krulwich, B., and Burkey, C. (1996). Learning user information interests through extraction of semantically significant phrases. Proceedings of the AAAI Spring symposium on Machine Learning in Information Access.

Lang, K. (1995). Newsweeder: Learning to Filter Netnews. Proceedings of the Machine Learning conference, Tahoe City, Calif, 331-339.

Lee, W. S. (2001). Collaborative learning for recommender systems. In Proccedings of the International Conference on Machine Learning. Department of Computer Science, National University of Singapore, Singapore.

Linden, G., Smith, B., and York, J.(2003). Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7(1), 76-80.

Marsden, Peter and Nan Lin (eds.) 1982. Social structure and network analysis. Beverly Hills: Sage.

M. Balabanovic and Y. Shoham (1997). Fab: Content-based,collaborative recommendation. Communications of the ACM, 40( 3), 66-72,.

M.Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin (1999). Combining content-based and collaborative filters in an online newspaper in Proceedings of ACM-SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, California, USA, 133-135.

McKinney, Yoon, & Zahedi（2002）. The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach. Information Systems Research, 13(3), 296-315.

Miller, B. N., I. Albert, S. K. Lam, J. A. Konstan, and J. Riedl (2003). MovieLens Unplugged: Experiences with an Occasionally Connected Recommender System. In Proceedings of the International Conference on Intelligent User Interfaces, Miami, Florida .

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw Hill.

Q. Li and B. M. Kim (2003). Clustering Approach for Hybrid Recommender System. in Proc. of the IEEE/WIC International Conference on Web Intelligence, 33-38,.

Q. Li and B. M. Kim (2003). Clustering approach for hybrid recommender system. in Proceedings of the IEEE/WIC International Conference on Web Intelligence, South Korea, October, 33-38.

Resnick, P., N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl (1994). GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 Computer Supported Cooperative Work Conference.

Robinson, J. P., haver, P. R., & Wrightsman, L.S. (1991). Measures of personality and social psychological attitudes. Volume 1: Measures of social psychological attitudes. San Diego, CA: Academic Press.

Sarwar, B., Karypis, G., Konstan, J., andRiedl, J. (2000), Analysis of recommendation algorithms fore-commerce. Proceeding of the 2nd ACM conference on E-Commerce, 158-167.

Schafer, J.B., Frankowski, D., Herlocker, J., and Sen, S. (2007). Collaborative Filtering Recommender Systems. ACM Digital Library. The Adaptive Web, Springer Verlag, Heidelberg.

Sebeok, T.A. and Zep, V.J. (1958). An Analysis of Structured Content of Cheremis Charms. Language and Speech, 1, 181-193.

Shardanand, U. and P. Maes (1995). Social information filtering: Algorithms for automating‘word of mouth’. In Proc. of the Conf. on Human Factors in Computing Systems.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters,27(8),861-874

Weber, R.P. (1985). Basic Content Analysis. Beverly Hills, Calif.: Sage Publications,.

Xinrui Zhang and Hengshan Wang (2005). Study on Recommender Systems for Business-To-Business Electronic Commerce. Communications of the IIMA, 5(4), 53-62.