臺灣博碩士論文加值系統

由於大多數的推薦系統對於替換率較高的產品，例如隨著流行或是季節更動的產品，使用傳統的購物籃分析或是協同過濾方法找出與目標顧客有相似興趣的顧客群的方式，並沒有辦法將一些新的產品推薦給顧客，因為這些產品尚未被購買，且顧客尚未對這些新產品有評鑑。相同地，對於一些較少被購買的產品，例如家具家電類，由於其被購買的次數較少，被顧客評比的紀錄也較少，因此被推薦的機率也相對地降低。
本研究嘗試以產品的特徵為基礎來分析顧客的消費行為，從顧客的交易紀錄以及產品的特徵資料庫中，分析顧客對於某些產品特徵之偏好，藉由所找出的顧客偏好輪廓規則，推薦可能會吸引顧客購買的產品。本研究的優點是，對於新的產品以及較少被購買的產品，只要符合顧客之輪廓，仍能推薦給該顧客，可解決傳統購物籃分析或是協同過濾方法無法將尚未被購買或是購買次數低的產品推薦給顧客的問題。本研究亦利用資料探勘之分群技術，使用兩階段分群法有效地找出與目標顧客具相同喜好之鄰近顧客，以推薦顧客之潛在需求。本研究經由探勘顧客之偏好輪廓規則可解釋推薦之結果，同時經由分析顧客對於產品特徵之喜好，可作為企業發展新產品之參考，而企業也可應用一對一之行銷策略以提高獲利能力。

In this paper, we construct a recommender system based on product features and the analysis of customer behaviors. We try to analyze why customers prefer some features of products from transaction databases and product databases, and then recommend the products that may attract customers. The advantage of this paper is to solve the disadvantages of market basket analysis and collaborative filtering that are not able to recommend products which are not purchased previously or only purchased a few times. This research also makes use of a two-phased clustering technique that combines advantages of the Self-Organized Map and K-means Clustering techniques to find the neighbors of the target customers efficiently and recommend products based on their potential interests. Through mining customers’ preferences, we can find the reasons of the recommendations. Besides, it can help enterprises to develop new products based on customers’ preferences and use one-to-one marketing to increase profits.

圖次 vi
表次 viii
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的 3
第四節 研究流程 4
第五節 研究假設與限制 5
第二章 文獻探討 7
第一節 一對一行銷 7
第二節 個人化 10
第三節 資訊檢索與資訊過濾 13
第四節 推薦系統 15
第五節 過去推薦系統之相關研究 19
第六節 分群 21
第七節 小結 27
第三章 研究方法 29
第一節 問題描述 29
第二節 研究架構 30
第二節 產品輪廓與顧客輪廓模組 31
第四節 顧客群集模組 37
第五節 範例說明 47
第四章 實驗設計與結果 53
第一節 實驗環境與工具 53
第二節 實驗資料內容 53
第三節 實驗評估 57
第四節 實驗方法與結果 59
第五章 結論與未來方向 81
第一節 結論與研究貢獻 81
第二節 未來研究方向 83
參考文獻 87

1. 余小勻，“電子商務一對一行銷”，資訊與電腦第239期，2000年6月，頁78-81。
2. 杜宜潔，整合網站使用探勘與網站內容探勘建構線上個人化新聞資訊推薦系統之研究，天主教輔仁大學資訊管理學系碩士論文，2002。
3. 孫美君，影響網站忠程度之因素研究－以購物型網站為例－，私立中原大學資訊管理學系碩士論文，2000。
4. 葉怡成，類神經網路模式應用與實做，台北市，儒林，1994。
5. 張倩茜譯，亞瑟．休斯著，資料庫行銷實用策略，台北市，美商麥格羅．希爾，2001。
6. 羅健銘，協同過濾於網站推薦之研究，國立台北科技大學商業自動化與管理研究所碩士論文，2001。
7. 賴宏仁，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學系研究所博士論文，1999。
8. Abidi, S. S. R., and Ong, J. “A Data Mining Strategy for Inductive Data Clustering: A synergy Between Self-Organizing Neural Networks and K-means Clustering Techniques,” IEEE TENCON 2000 International Conference, Kuala Lumpur, 2000, pp.568-573.
9. Adomavicius, G. and Tuzbilin, A., “Using Data mining Methods to Build Customer Profiles,” IEEE Computer Society DL, Vol. 34, No. 2, 2001, pp.74-82.
10. Agrawal, R.,and Srikant, R., “Fast Algorithm for Mining Association Rules,” Proceedings of the First International Conference on Very Large Database, Framingham, 1994, pp. 181-192.
11. Anderberg, M.R. Cluster Analysis for Applications, Academic Press, New York, 1973, pp.359.
12. Balakrishnam, P.V.,Cooper, M.C., Jacob,.V.S., ans Lewis,P.A., “A Study of the Classification Capabilities of Neural Networks Using Unsupervised Learning-A Comparison K-Means Clustering,” Psychometrika, Vol.59, No.4, 1994, pp.509-525.
13. Chen, M.S., Han, J., and Yu, P. S, “Data Mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering, Vol 8, No.6, 1996, pp. 866-883.
14. Dean, R., “personalizing your web site”, http://builder.cnet.com/webbuilding/pages/Business/Personal/, 2002/10/20.
15. Ghani, R. and Fano, A., “Building Recommender Systems using a Knowledge Base of Product Semantics,” Workshop on Recommendation and Personalization in eCommerce, Malaga, 2002.
16. Han, J. and Kamber, M., Data Mining concepts and techniques, Morgan Kaufmann, New York, 2001.
17. Han, J. and Pei, J., “Mining Frequent Patterns without Candidate Generation”, Proceedings of the ACM SIGMOD international Conference on Management of Data, 2000, Dallas, pp.1-12.
18. Herlocker, L. J., Konstan, J. A., and Riedl, J., “Explaining Collaborative Filtering Recommendations,” Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, 2000, Philadelphia, pp.241-250.
19. Kohonen, T., Self-Organization and Associative Memory, Springer-Verlag, Berlin, 1989.
20. Kolatch, E., ”Clustering Algorithms for Spatial Databases: A Survey,” Proceedings of the 9th International Symposium on Spatial Data Handling (SDH2000), Beijing, 2000,
21. Kotler, P., Marketing Management: Analysis, Planning, Implementation, and Control, Prentice Hall International 9ed, 1997, pp.313-314.
22. Krulwich, B. and Burkey, C., “The InfoFinder Agent: Learning User Interests through Heuristic Phrase Extraction,” IEEE Expert, Vol. 12, No.5, 1997, pp.22-27.
23. Lang, K., “NewsWeeder: Learning to Filter Netnews,” Proceedings of the 12th International Conference on Machine Learning, Morgan Kaufmann, 1995, pp.331-339.
24. Lawrence, R.D., Almasi, G.S., Kotlyear, V., Viveros, M.S., and Duri, S.S., “Personalization of Supermarket Product Recommendations,” Data Mining and Knowledge Discovery, Santa Barbara, 2001, pp.11-32.
25. MacQueen, “Some methods for classification and analysis of multivariate observations,” Proceedings of the Fifth Berkeley Symposium on Mathematical statistics and probability, Losangles, 1967, pp.281-297.
26. Mobasher, B., “WebPersonalizer: A Server-side recommender systems,” Technical Report Database, 2001.
27. Peppers, D., Rogers, M., and Dorf, B., “Is Your Company Ready for One-to-One Marketing?,” Harvard Business Review, 1999, Vol. 77, Issue 1, pp.151.
28. Resnick ,P., Iacovou, N., Suchak, M., and Bergstrom,P., and Riedl, J., “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proceedings of CSCW 94 Conference on Computer Supported Cooperative Work, Chapel Hill, 1994, pp.175-186.
29. Saracevic, T., Kantor, P., Chamis, A.Y., and Trivision, D., “A Study in Information Seeking and Retrieving. I. Background and Methodology,” Journal of the American Society for Information Science, Vol. 39, No. 3, 1998, pp.161-175.
30. Saracevic, T., Kantor, P., Chamis, A.Y., and Trivision, D., “A Study in Information Seeking and Retrieving. II. User, Questions and Effectiveness,” Journal of the American Society for Information Science, Vol. 39, No. 3, 1998, pp.176-195.
31. Schafer, J. B., Konstan, J. and Riedl, J. “Recommender Systems in E-Commerce,” ACM Conference on Electronic Commerce, Tampa, 1999, pp.158-166.
32. Vandermerwe, S., “How Increasing Value to Customers Improves Business Results,” Sloan Management Review, 2000, Vol. 42, No 1, pp.27-37.
33. Vesanto, J. and Alhoniemi, E. “Clustering of the Self-Organizing Map,” IEEE Transactions on Neural Networks, 2000, Vol.11, No.3, pp.586-600.
34. Walker, B. A., Celsi, Richardand O., Jerry C., “Exploring the structural Characteristics of Consumer’s Knowledge,” Advance in Consumer Research, 1986, pp.17-21.
35. Wu, K.L., Aggarwal, C. C. and Yu, P. S., “Personalization with Dynamic Profiler,” IEEE, California 3rd International Workshop on Avanced Issues of E-Commerce and Web-based Information Systems（WECWIS）, San Jose, 2001, pp.12-20.
36. Yu, P.S., “Data Mining and Personalization Technologies,” IEEE, Taiwan 6th International Conference on Database Systems for Advanced Applications（DASFAA）, Hsinchu, 1999, pp.6-13.