現階段有企業編列大量的預算投入網路傳播媒體當中，雖然可以提高企業的知名度，但是卻吸引不了大多數的消費者消費，因為現代的消費者由於積極的交流訊息，因此已經不再完全相信企業傳來的資訊。實際影響消費者購買行為的主要關鍵為消費者之間的口耳相傳，因此，如果能夠將社群網路中較積極且具有價值的會員推薦給其他會員認識進而成為好友，相信藉由同儕團體的關係產生門檻效果、示範效果，會使得其他消費者的消費如滾雪球般的現象，另外，也能夠產生如店員般的會員給予消費者幫助。因此，本研究希望以業界真實資料做分析能夠有效的產生符合個人化推薦，並且使得往後企業透過本研究機制，能夠以較低的營運成本更有效的促進消費者消費。
本研究所提出的模組化推薦機制分為兩階段，第一階段為群集分析，以RFM為顧客特徵進行兩階段式群集分析，分析後找出具有意義的群集數，再將結果加以定義並且命名；在第二階段，本研究應用霍普菲爾網路做為推薦法則，利用第一階段所找到的VIP顧客群與具有潛力顧客群的留言關係做為訓練範例，以過去具有參考價值會員的交友風格做為典範來聯想欲推薦會員的交友風格。實驗結果得到推薦準確率為9.4%，搜全率為56.3%以及F1指標值為16.1%。

Nowadays, a great deal of firms are preparing tremendous budgets into internet media. It could not attract most consumers to purchase their products though it did make companies’ name famous. That is because today’s consumers communicate each other so actively that they don’t completely believe information from these firms. The key to affecting consumers’ purchasing behaviors would be the word-of-mouth advertising among consumers. As a result, if we can introduce those active and valuable members in community to others and make them become friends, consumptions will snowball as a demonstration by the relationship among colleagues. Moreover, these members will give consumers assistance just like sales clerks. Therefore, this research is to produce recommendation of customization by the analysis of actual business data. And then firms can use lower operation budgets to prompt consumers’ purchasing through this research system.
There are two steps of module recommender system in this research offered by our institute. The first step is cluster analyze. We proceed two-stage procedure cluster analyze of SOM+K-means for customers’ characters via RFM. After that we locate meaningful cluster numbers, defining the results and naming them. In the second step, this research applies Hopfield net as a recommendation principle, and uses the messages of VIP and potential customer groups in the first stage as a training demonstration. So we can infer recommended members’ friendship style from valuable members’ in the past. The results of this research are that precision is 9.4%, recall is 56.3% and F1 metric is 16.1%.

摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 ix
第一章 序論 1
1.1 研究背景與動機 1
1.2 研究目的 4
第二章 文獻探討 6
2.1 RFM模型 6
2.1.1 RFM定義 6
2.1.2 RFM指標分數建構原則 7
2.2 群集分析 7
2.2.1 群集分析程序 8
2.2.2 群集分析衡量指標 10
2.2.3 群集分析研究發展與應用 11
2.3 個人化 12
2.3.1 個人化服務流程 13
2.3.2 個人化資訊的相關研究與應用 15
2.4 推薦系統 16
2.4.1 內容導向式推薦 17
2.4.2 協同式推薦 18
2.4.3 混合式推薦 19
2.4.4 推薦系統之評估方法 19
2.4.5 推薦系統之相關研究與應用 21
第三章 研究方法 24
3.1 研究架構 24
3.2 變數選擇 25
3.3 資料分析與前置處理 26
3.4 K-means Algorithm 27
3.5 自組織映射圖網路(Self-Organizing Map，SOM) 29
3.5.1 側向交互作用 29
3.5.2 網路架構 30
3.5.3 網路演算法 32
3.6 Two-Level Approach 35
3.7 霍普菲爾網路(Hopfield net，HNN) 35
第四章 實驗分析 39
4.1 資料分析 39
4.2 群集分析過程與結果 40
4.3 假設檢定 43
4.4 系統推薦 43
4.4.1 資料前置處理 43
4.4.2 推薦過程與結果 47
第五章 結論 51
5.1 成果與貢獻 51
5.2 未來研究方向 52
參考文獻 53

[1] Adomavicius G,Tuzhilin A., "Toward the Next Generation of Recommender Systems:A survey of the State-of-the-Art and Possible Extensions," IEEE Transaction on Knowledge and Data Engineering, 2005, pp.734-749.
[2] Adomavicius, G., and Tuzbilin, A., "Using data mining methods to build customer profiles," IEEE Computer Society Press, 34(2), 2001, pp.74-82.
[3] Bandyopadhyay, S. and Maulik, U., "Nonparametric Genetic Clustering: Comparison of Validity Indices," IEEE Transactions on System, Man, and Cybernetics-Part C: Applications and Reviews, vol. 31, no. 1, 2001, pp.120-125.
[4] Bezdek, J. C. and Pal, N. R.," Some New Indexes of Cluster Validity," IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 28, no. 3, 1998, pp.301-315.
[5] Cliff Allen, Deborah Kania, Beth Yaeckel, Internet WORLD guide to one-to-one web marketing, John Wiley & Sons, 1998.
[6] D. Billsus and M. Pazzani," Learning Collaborative Information Filters," Proc. Int’l Conf. Machine Learning, 1998.
[7] D. Goldberg, D. Nichols, B.M. Oki, and D. Terry," Using collaborative filtering to weave an information tapestry," Communications of the ACM, vol. 35, no. 12, 1992, pp.1-10.
[8] D.M. Pennock and E. Horvitz," Collaborative Filtering by Personality Diagnosis: A Hybrid Memory And Model-Based Approach," Proc. Int’l Joint Conf. Artificial Intelligence Workshop: Machine Learning for Information Filtering, 1999, pp. 473-480.
[9] Davies, D. L. and Bouldin, D. W.," A Cluster Separation Measure," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.1, no.4, 1979, pp. 224-227.
[10] E. Backer and A. Jain," A clustering performance measure based on fuzzy set decomposition," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-3, no. 1, 1981, pp.66–75.
[11] Huang, Z., Chung, W., Ong, T. and Chen, H.," A Graph-based Recommender System for Digital Library," In Proceedings of JCDL''02, July 13-17, 2002, Portland, Uregon, USA, pp 65-73.
[12] Hughes, A. M., Strategic Database Marketing, Chicago：Probus Publishing, 1994.
[13] J. J. Hopfield, D. W. Tank," Neural computation of decisions in optimization problems," Biological Cybernetics, vol.52, 1985, pp.141-152。
[14] J.S. Breese, D. Heckerman, and C. Kadie," Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. 14th Conf. Uncertainty in Artificial Intelligence, Morgan Kaufman, 1998, pp.43-52.
[15] Jain, A.K., Murty, M.N. and Flynn, P.J.," Data Clustering: A review," ACM Computing Survey, vol.31, no.3, 1999, pp.264-323.
[16] Janal, Daniel S. Dan Janal''s guide to marketing on the Internet：Getting people to visit, buy, and become customers for life. New York: J. Wiley, 2000.
[17] Juha, V. and Esa, A.," Clustering of the Self-Organizing Map," IEEE Transactions on Neural Networks, vol.11, no.3, 2000, pp.586-600.
[18] K. Goldberg, T. Roeder, D. Gupta, and C. Perkins," Eigentaste: A Constant Time Collaborative Filtering Algorithm," Information Retrieval J., vol. 4, no. 2, 2001, pp.133-151.
[19] Kohonen, T.," Self-Organizing Map," Proc. IEEE, vol.78, no.9, 1990, pp.1464-1480.
[20] M. Pazzani and D. Billsus," Learning and Revising User Profiles: The Identification of Interesting Web Sites," Machine Learning, vol. 27, 1997, pp. 313-331.
[21] Maria, H., Yannis, B. and Michalis, V.," On Clustering Validation Technique," Journal of Intelligent Information Systems, vol. 17, no.2–3, 2001, pp.107-145.
[22] Maulik, U. and Bandypadhyay, S.," Performance Evaluation of Some Clustering Algorithms and Validity Indices," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no.12, 2002, pp.1650-1654.
[23] McKenna K.Y.A., and Bargh J.A. ," Coming out in the age of the Internet:identity demarginalization through virtual group participation," Journal of Personality and Social Psychology, vol.73, 1998, pp.681-694.
[24] NTT Media Scope, 丁小艾譯, 廣告管不住消費者:社群行銷VS.非廣告年代, 中國生產力中心, 2005, pp.22-26.
[25] P. Mangiameli, S. K. Chen, and D.West," A comparison of SOM neural network and hierarchical clustering methods," Eur. J. Oper. Res., vol. 93, no. 2, 1996, pp. 402-417.
[26] P. Resnick and R. Varian," Recommender Systems," Communications of the ACM, vol.40, no. 3, 1997, pp.56-58.
[27] R.J. Mooney and L. Roy," Content-Based Book Recommending Using Learning for Text Categorization," Proc. ACM SIGIR ’99 Workshop Recommender Systems: Algorithms and Evaluation, 1999, pp.195-204.
[28] Sarwar, B., Karypis, G.., Konstan, J., and Riedl, J.," Analysis of recommendation algorithms for e-commerce," Proceeding of the ACM E-Commerce, 2000, pp. 158-167.
[29] Schafer, J. B., Konstan, J. A., and Riedl, J.," E-Commerce Recommendation Applications," Journal of Data Mining and Knowledge Discovery, vol. 5, no. 1, 2000, pp.115-152.
[30] Stacy E.Bressler & Charles E. Grantham,Sr., 蔡舜玉、丁惠民譯, 社群行銷, 美商麥格羅．希爾國際出版公司台灣分公司, 2002, pp. 286-288.
[31] Stone, B., Successful Direct Marketing Methods, Lincolnwood, IL：NTC Business Books, pp. 37-59.
[32] U. Shardanand and P. Maes," Social Information Filtering: Algorithms for Automating ‘Word of Mouth’," Proc. Conf. Human Factors in Computing Systems, 1995, pp.210-217.
[33] Weiss, S, M., and Indurkhya, N., Predictive Data Mining: A Practical Guide, San Francisco, CA: Morgan Kaufmann, 1998.
[34] Wind, Y. ," Issues and Advances in Segmentation Research," Journal of Marketing Research, vol.XV, no.8, 1978, pp.317-337.
[35] Y. Zhang, J. Callan, and T. Minka," Novelty and Redundancy Detection in Adaptive Filtering," Proc. 25th Ann. Int’l ACM SIGIR Conf., 2002, pp.81-88.
[36] 李麗華、陳致傑 ,「一個新的代理人智慧型網路廣告行銷架構」, 第二屆網站經營學術暨實務研討會, 2000, pp.159-167。
[37] 張瀚仁,「個人化技術對虛擬社群發展之影響」, 碩士論文，國立政治大學資訊管理學系, 2000。
[38] 許智為，「應用巢狀式群集分析方法改善顧客區隔效度之研究」，碩士論文，國立台北科技大學工業工程與管理系，2006。
[39] 陳宏泰，「個人化網路廣告推薦之研究」，碩士論文，國立暨南國際大學資訊管理學系，2002。
[40] 曾憲雄，蔡秀滿，蘇東興，曾秋榮，王慶堯，資料探勘，台灣：旗標出版股份有限公司，2005。
[41] 程士豪，「個人化網友推薦系統之建立與效度改善 -- 應用於某社群網站」，碩士論文，國立台北科技大學工業工程與管理系，2006。
[42] 楊振偉, 「利用書籤功能達到網際網路資訊分享與過濾的技術探討」, 碩士論文，國立中央大學資訊管理研究所, 1998。
[43] 廖鐿鈤，「網路入侵—虛擬世界對真實世界之滲透」，碩士論文，南華大學社會學研究所，2001。
[44] 劉家儀，「以人際關係論與計畫行為論探討網路交友之現象」，碩士論文，國立中山大學資訊管理研究所，2000。
[45] 劉德敏，「網友推薦系統之研發與建置」，國立台北科技大學學報，第三十九之一期，pp.85-102。
[46] 羅家德 , EC大潮-電子商務趨勢, 聯經, 2000,pp. 93-102。
[47] 羅淑娟，電子商務技術實驗室成員，「顧客分群分析報告」，國立台北科技大學，技術報告，民國93年。