後續建議

推薦系統的發展隨著應用技術與領域的不同而不斷演進，本研究對於將社會 網路應用於推薦系統做了深入的研究，然而由於系統實驗仍有其限制，後續仍然 有許多地方值得探討，以下針對後續研究方向提出建議：

1. 建立主題知識本體(Ontology)

在進行主題萃取的過程中，利用階層式分群法以樹狀結構表示主題分群 之結果，產生主題概念階層，可發掘主題概念之從屬關係。經由使用者主題 偏好之關聯，建立主題概念之連結，以形成主題之知識本體，可幫助使用者 瞭解本身處於何種階層層級，未來可朝那些研究方向前進。

2. 使用者評分之應用

使用者評分可分為明顯性評分與隱含性評分。明顯性評分為使用者依對 目標物感興趣程度給予主觀評分，例如要求使用者在閱讀完文件後給予評 分；隱含性評分的估計通常以使用者的瀏覽行為做依據，例如所花費的閱讀 時間以及滑鼠的操作等。經由使用者評分可以更精確瞭解使用者偏好所在，

使資訊推薦更符合使用者需求。

3. 社會網路之階層擴展

社會網路有助於社群之形成，本研究著眼於與使用者本身有直接關聯的

社會網路關係，未來可經由建立在共同社會網路中之使用者關係，進一步探 討社會網路之資訊流動及影響。例如使用Floyd-Warshall演算法可找出位於 同一社會網路中，兩兩使用者間的最短路徑，則可經由節點的分析，研究其 對使用者的影響。

參考文獻

[1] A. Iskold, (2007) “The Art, Science and Business of Recommendation Engines.”

http://www.readwriteweb.com/archives/recommendation_engines.php [2] A. K. Jain, M. N. Murty, & P. J. Flynn, “Data clustering: A review,” ACM

Computing Surveys, vol. 31, pp. 264-323, 1999.

[3] B. Krulwich, & C. Burkey, “The InfoFinder agent: Learning user interests through heuristic phrase extraction,” IEEE Expert: Intelligent Systems and Their

Applications, vol. 12, pp. 22-27, 1997.

[4] B. Sarwar, G. Karypis, J. Konstan, & J. Riedl, “Analysis of recommendation algorithms for e-commerce,” Proceedings of the 2nd ACM conference on

Electronic commerce, pp. 158-167, 2000.

[5] D. Goldberg, D. Nichols, B. M. Oki, & D. Terry, “Using Collaborative Filtering to Weave An Information Tapestry,” Communications of the ACM, vol. 35, pp.

61-70, 1992.

[6] D. Koller, & M. Sahami, “Hierarchically classifying documents using very few words,” Proceedings of 14th the International Conference on Machine Learning, pp.170–178, 1997.

[7] F. Sebastiani, “Machine learning in automated text categorization,” ACM

Computing Surveys, vol. 34, pp. 1-47, 2002.

[8] G.. Karypis, E. H. Han, & V. Kumar, “Chameleon: Hierarchical clustering using dynamic modeling,” Computer, vol. 32, pp. 68-75, 1999.

[9] H. C. Chang, & C. C. Hsu, “Using topic keyword clusters for automatic document clustering,” Transactions on Information and Systems, vol. 88, pp.

1852-1860, 2005.

[10] H. Hotta , “User profiling system using social networks for recommendation”, In

Proceedings of 8th International Symposium on Advanced Intelligent Systems ,

[11] H. Kautz, B. Selman, & F. Park, “Referral Web: Combining social networks and collaborative filtering,” Communications of the ACM, vol. 40 , pp. 63-65, 1997.

[12] H. Sakagami, & T. Kamba, “Learning Personal Preferences on Online Newspaper Articles from User Behaviors,” Computer Networks and ISDN

Systems, vol. 29, pp. 1447-1455, 1997.

[13] J. B. Schafer, J. Konstan, & J. Riedi, “Recommender systems in e-commerce,”

Proceedings of the 1st ACM conference on Electronic commerce, pp. 158-166,

observations,” Proceedings of 5th Berkeley Symposium on Mathematical

Statistics and Probability, vol. 1, pp. 281-297, 1967.

[15] J. Makhoul, F. Kubala, R. Schwartz, & R. Weischedel, “Performance measures for information extraction,” Proceedings of DARPA Broadcast News Workshop, pp. 249-252, 1999.

[16] J. Moreno, Who Shall Survive? New York: National Institute of Mental Health, 1934.

[17] J. R. Tyler, D. M. Wilkinson, & B. A. Huberman, “Email as spectroscopy:

Automated discovery of community structure within organizations,”

Communities and technologies, pp. 81-96, 2003.

[18] J. Rucker, & M. J. Polanco, “Siteseer: Personalized navigation for the web,”

Communications of the ACM, vol. 40, pp. 73-76, 1997.

[19] K. C. Gowda, & G. Krishna, “Agglomerative clustering using the concept of mutual nearest neighbourhood,” Pattern Recognition, vol. 10, pp. 105-112, 1978.

[20] K. Faust, “Comparison of methods for positional analysis:Structural and general equivalences,” Social Networks, vol. 10, pp.313-341, 1988.

[21] L. C. Freeman, “Centrality in Social Networks: Conceptual clarification,” Social

Networks, vol. 1, pp. 215-239, 1979.

[22] L. Page, & S. Brin, “The anatomy of a large-scale hypertextual Web search

engine,” In Proceedings of the seventh international World-Wide Web conference, 1998.

[23] L. Garton, C. Haythornthwaite, & B. Wellman, (1997) “Studying Online Social Networks,” http://jcmc.huji.ac.il/vol3/issue1/garton.html

[24] M. A. Shah, “ReferralWeb: A resource location system guided by personal relations,” Master's thesis, M.I.T., 1997.

[25] M. Granovetter, “The strength of weak ties: A network theory revisited,”

Sociology Theory, vol. 1, pp. 201-233, 1983.

[26] N. Zhong, J. Liu & Y. Yao, “In search of the wisdom web,” Computer, vol. 35, pp. 27-31, 2002.

[27] P. Athanasios, Probability, Random Variables and Stochastic Processes., Second Edition ed. New York: McGraw-Hill, 1984.

[28] P. Mika, “Flink: Semantic Web technology for the extraction and analysis of social networks,” Web Semantics: Science, Services and Agents on the World

Wide Web, vol 3, pp. 211-223, 2005.

[29] P. Pattison, Algebraic models for social networks., Cambridge University Press, 1993.

[30] S. E. Chan, R. K. Pon, & A. F. Cárdenas, “Visualization and Clustering of Author Social Networks,” International Conference on Distributed Multimedia Systems

Workshop on Visual Languages and Computing, pp. 30-31, 2006.

[31] S. P. Borgatti, (1998) “What Is Social Network Analysis?”

http://www.analytictech.com/networks/whatis.htm

[32] S. Staab, P. Domingos, P. Mike, J. Golbeck, D. Li, T. Finin, A. Joshi, A. Nowak,

& R. R. Vallacher, “Social networks applied,” IEEE Intelligent Systems, vol. 20, pp. 80-93, 2005.

[33] V. Kotlyar, M. S.Viveros, S. S. Duri, R. D. Lawrence, & G. S. Almasi, “A case study in information delivery to mass retail markets,” In Proceedings of the 10th

International Conference on Database and Expert Systems, 1999.

[34] X. Liu, J. Bollen, M. L. Nelson, & H. V. de Sompel, “Co-authorship networks in the digital library research community,” Information Processing and

Management, vol 41, pp. 1462-1480 , 2005.

[35] Y. Matsuo, J. Mori, & M. Hamasaki, “POLYPHONET: An advanced social network extraction system from the Web ", Web Semantics: Science, Services

and Agents on the World Wide Web, vol. 5, pp. 262-278, 2007.

[36] Kappa Statistics - http://www.dmi.columbia.edu/homepages/chuangj/kappa [37] LingPipe NLP Toolkit - http://alias-i.com/lingpipe/

[38] NCTUIR - http://ir.lib.nctu.edu.tw/

[39] Porter Stemming Algorithm - http://tartarus.org/~martin/PorterStemmer [40] D. J. Watts 著, 傅士哲, 謝良瑜譯, “6 個人的小世界” 大塊文化, 2004.

[41] 楊永芳, “語意擴充式文件推薦方法之研究” 中山大學資訊管理研究所碩士 論文, 2001.

[42] 翁明正, 翁頌舜, “應用本體論與社會網路分析於個人化推薦” 2007 數位科技 與創新管理研討會論文集, 423-434, 2007.