在競爭如此激烈的環境下，推薦適當的產品以吸引顧客並滿足他們的需求是企業生存的關鍵。而電子商務的應用之一，推薦系統（Recommender systems），可以用來支援產品的個人化推薦。

不同的推薦系統採用的演算法各異，譬如近年來所提出的一種演算法，加權RFM基礎法(weighted RFM-based method; WRFM-based method)，是根據顧客終身價值（customer lifetime value）來推薦，其採用顧客的三種特徵值:最近購買日（Recency）、購買頻率（Frequency）及購買金額（Monetary），而加權RFM基礎法被證實可提升忠誠顧客的滿意度。

另一種演算法，偏好基礎協同過濾法（Preference-based collaborative filtering method; Preference-based CF method），可基於顧客風險偏好的相似度來提供產品推薦，該演算法亦被證實能夠改善對於低忠誠度顧客的推薦品質。上述兩種演算法最終皆採關聯法則基礎推薦（Association rule based recommendation）來推薦商品。

本研究試圖融合上述兩種方法的優點，提供一種改善推薦品質的混合法，並以國內某銀行信託部銷售的基金金融商品資料集為例，加以評估各方法的推薦品質。實驗結果指出顧客的風險偏好對於推薦的效果比其終身價值大；本個案經關聯法則過濾出的規則大都相當有趣，因此本混合法對於Liu和Shih之混合法的改進並不顯著，但是對於金融商品的推薦仍有相當的品質，最後，分析各群體的風險偏好特徵，本混合法確實能夠提供未來金融商品個人化設計的參考依據。

In a fiercely competitive environment, it is essential that firms can recommend proper products to attract customers and meet their needs. Such recommender systems have now emerged in e-commerce applications to support the recommendation of personal products. Different recommender systems have adopted varied algorithms. Recently, a weighted RFM-based method (WRFM-based method) has been proposed to provide recommendations based on customer lifetime value, including Recency, Frequency, and Monetary. Another algorithm, Preference-based collaborative filtering (CF), typically makes recommendations based on the similarities of customer preferences. The mentioned two algorithms adopt the association rule based recommendation to recommend products. This study proposes a hybrid method that will uncover the merits of the WRFM-based method and the preference-based CF method to improve the quality of recommendations. Experiments are conducted to evaluate the quality of recommendations provided by the proposed method by using a data set concerning the fund marketing. The experimental results indicate that the recommendatory effect of preference outperforms a customer’s lifetime value. The proposed hybrid method outperforms the WRFM-based method and the preference-based CF method. Finally, by analyzing the risk preference of each customer clusters, the proposed hybrid method can indeed provide the basis for personal recommendation in the future.

莊筑因，2001，「新興風險管理工具-風險值（VaR）之探討」，中華民國證券櫃檯買賣中心月刊，第70期。
Agrawal, R., Imielinski, T., Swami, A., 1993. Mining association between sets of items in large database. In: Proceedings of the ACM-SIGMOD Conference, pp. 207–216.
Agrawal, R., Srikant, R., 1994. Fast algorithms for mining association rules. In: Proceedings of the VLDB conference, pp. 407–419.
Berger, P., Nasr, N., 1998. Customer lifetime value: marketing models and applications. Journal of Interactive Marketing 12 (1), pp. 17–30.
Bult, J.R., Wansbeek, T.J., 1995. Optimal selection for direct mail.Marketing Science 14 (4), pp. 378–394.
Chen, H.C., Chen, A.L.P., 2001. A music recommendation system based on music data grouping and user interests. In: Proceedings of the ACM Conference (Information and Knowledge Management),pp. 231–238.
Chen, M.S., Han, J., Yu, P.S., 1996. Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8 (6), pp. 866–883.
Hearst, M., 1998. K-means clustering, UCB SIMS, Fall. Available from <http://www.sims.berkely.edu/courses/is296a-3/f98/lectures/ui-bakground/sld025.htm>.
Engel ,J.F., Blackwell ,R.D.,& Miniard ,P.W., Consumer behavior , 1993,7th ED, The Dryden Press, Chicago.
Hill, W., Stead, L., Rosenstein, M., Furnas, G., 1995. Recommending and evaluating choices in a virtual community of use. In:Proceedings of the ACM (CHI_95), pp. 194–201.
Hughes, A.M., 1994. Strategic Database Marketing. Probus Publishing, Chicago.
Irvin, S., 1994. Using lifetime value analysis for selecting new customers. Credit World 82 (3), pp. 37–40.
John C. Hull, Option, Futures, and other Derivatives, 2003, 5th ED, Pearson Education, New Jersey.
Jorion, P., Value at Risk：The New Benchmark for Controlling Market Risk, 1997, IL：Irwin,Chicago.
Kahan, H., 1998. Using database marketing techniques to enhance your one-to-one marketing initiatives. Journal of Consumer Marketing 15 (5), pp. 491–493.
Kotler , Philip and Gary Armstrong, Principles of Marketing, 1999, 8th ED., Prentice-Hall ,New Jersey .
Lang, K., 1995. Newsweeder: learning to filter Netnews. In: Proceedings
of the Machine Learning Conference, pp. 331–339.
Liu, D.R., Shih, Y.Y., 2005a. Integrating AHP and data mining for
product recommendation based on customer lifetime value. Information
and Management 42(3), pp.387-400.
Liu, D.R., Shih, Y.Y., 2005b. Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences. The Journal of Systems and Software 77(2), pp. 181–191.
MacQueen, J.B., 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability conference, pp.281–296.
M. Balabanovic and Y. Shoham, 1997.Fab : Content-Based, Collaborative Recommendation, Communications of the ACM , 40(3),pp. 88-89.
Marcus, C., 1998.A Practical Yet Meaningful Approach to Customer Segmentation, Journal of Consumer Marketing, 15(5), pp. 494-504.
Mehmed Kantardzic, DATA MINING Concepts,Models,Methods,and Algorithms,2003,John Wiley & Sons, Inc., New Jersey
Miglautsch, J., 2000. Thoughts on RFM scoring. Journal of Database Marketing 8 (1), pp. 67–72.
Miglautsch, J., 2001. Thoughts on RFM Scoring. International Society for Strategic Marketing(27), pp.1-7.
Mobasher,B., Cooley,R., Srivastava,J.,2000. Automatic personalization based on Web usage mining.Association for Computing Machinery. Communications of the ACM.43(8),pp. 142-152
Peppers, D., Rogers, M., 1997. The One to One Future: Building Relationships One Customer at a Time. Bantam Doubleday Dell Publishing.
Punj, G.N., Stewart, D.W., 1983. Cluster analysis in marketing research: review and suggestions for application. Journal of Marketing Research 20(2), pp. 134–148.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., 1994. GroupLens: an open architecture for collaborative filtering of Netnews. In: Proceedings of the CSCW Conference, pp. 175–186.
Resnick, Paul, Varian, Hal R. ,1997. Recommender systems. Association for Computing Machinery. Communications of the ACM. 40(3), pp. 56-59.
Rucker, J., Polanco, M.J., 1997. Personalized navigation for the Web.Communications of the ACM 40 (3), pp. 73–75.
Rusinko, C. A. and J. O. Mattews,1997.Evolution of A Technological Community: A Case Study of Financial Derivatives.Journal of Engineering and Technology Management 14(3-4), pp.315-336.
Saaty, T.L., 1994. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process. RWS Publications,Pittsburgh, PA.
Salton, G., McGill, M.J., 1983. Introduction to Modern Information Retrieval. McGraw-Hill, New York.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J., 2000. Analysis of recommendation algorithms for e-commerce. In: Proceedings of the ACM Conference (Electronic Commerce), pp. 158–167.
Schafer, J.B., Konstan, J.A., Riedl, J., 2001. E-commerce recommendation applications. Journal of Data Mining and Knowledge Discovery 5 (1/2), pp. 115–152.
Shardanand, U., Maes, P., 1995. Social information filtering: algorithms for automating ‘world of mouth’. In: Proceedings of the ACM (CHI_95), pp. 210–217.
van Rijsbergen, C.J., 1979. Information Retrieval, Second ed. Butterworths, London.