Y Jiang et al. Decision Support Systems 48(2010)470-479 customers'characteristics to achieve high satisfaction levels. There- 14 D.S. Broomhead, D Lowe, Multivariable functional interpolation and adaptive fore, the validity of customers'needs and preferences has an usper 151 Ac Charmes w 2 C De5,54) 515-512. ed game formulation of advertising strategies, Oftentimes consumers do not have clear needs and preferences. There- [6 A Chanes. W.w. Cooper, J.K. DeVoe, D.B. Learner, Demon: decision mapping via fore, finding an effective way to facilitate customers to express their true orks-a model for marketing new products, Management needs and preferences is essential for the recommendation systems. [7] A Chames, W.W. Cooper, K DeVoe, D B Learner, DEMON, Mark ll: an extremal 6. Epilogue [8]A. Charnes, W.W. Cooper, D B. Learner, Management science and marketing Professor W. W. Cooper, a pioneer researcher in management, [9] Y Chen, D Lig ntropy approach to feature selection in knowledge- has made a significant impact on the fields of decision sciences, oper- [10 K.W. Cheung. IT. Kwok, MH. Law, KC Tsui, Mining for agement. Among his contributions, Professor Cooper has paid much [111 JA. Chevalier, D Mayzlin, The effect of word of mouth on sales: online book reviews. attention to the research in the area of marketing. He developed in- urnal of Marketing Research 43 (3)(2006)345-354 novative models to optimize resource allocation for alternative media [ 12] YH. Cho, JK.Kim, SH.Kim,a alized recommender system based on web usage mining and decision tree induction, Expert Systems with Applications 23 (3) dvertising 5. In the 1960s, he and his associates built a strategic 02)329-342. decision model, DEMON, for marketing new products [ 6, 7]. His idea of 1 131 EK. Clemons, G. Gao, LM Hitt, When online reviews meet hyper differentiation: creating a decision support system to aid with marketing decision making inspires our pursuit of this research. [14] W.W. Cooper, LM Seiford, K. Tone, Data envelopment an Information technologies, especially Internet technology, bring text with models, applications, references and DEA-solver software, Kluwer significant influence to the traditional marketing environment and (15) C. Dellarocas. The digitization of word of mouth: promise and challenges of online colleagues [ 8] realized the importance of information technology to [ 16] M Deshpande, G. Karypis, Item-based top-N ithms. ACM marketing research. They argued that researchers and practitioners ransactions on Information Systems 22(1)(2004)143-177. should handle the "problems that may arise for the relations between [17 D. Goldberg, D. Nichols, B M. Oki, D. Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM 35(12)(19 marketing management and marketing research because of the rapidly [181 J Herlocker, JA Konstan, J. Loren, G. Terveen, T. Riedl, Collaborative filtering increasing use of personal computers. "Indeed, as the Internet becomes Systems22(1)(2004)5-5 a main part of modern society and online shopping develops into a 1191 M.Y. Hu, M Shanker, G.P. Zhang MS Hung Modeling consumer situational choice aily activity, online recommendation systems become ubiquitous and 44(4)(2008)899-908 widely utilized by practitioners to improve their revenues. Our research 120Y. Huang, L Bian, A Bayesian network and analytic hierarchy process based focuses directly on the improvement of recommendation systems In investigating the rating classification problem, we follow [211 LP. Hung. A personalized recommendation system based on product taxonomy for Dr Coopers insights about marketing research. In his opinion, when deal- one-to-one marketing online, Expert Systems with Applications 29(2)(2005) ing with decision-making problems under uncertainty, the marketing model should be"simple and intuitive, nd easy to understand by both [22) Y. Kim, W.N. Street, An intelligent system for customer targeting: a data mining eedings of the 2001 IEEE International Confer- ence on Data Mining. 2001: California, 2001. Therefore, the associative classification model can be understood and used [251TP Liang. Y.E. Yang D.N. Chen, Y.C. Ku, A semantic-expansion approach o personal- practitioners straightforwardly. Moreover, the outcome of our research [26] B Liu, WHsu, Y Ma, Integrating classification and association ruler is not limited to only the classification results. According to Dr. Cooper, ings of the Fourth International Conference on Knowledge Discovery and Data Mining imply predicting what will happen in the future is of less interest to DD-98). New York,1998,1998 managers than knowing what has to be changed, and by how much, to [27 YZ Liu, Y C Jiang. x Liu, SL Yang, CSMC: a combination strategy for multi-class classification based on multiple association rules, Knowledge-Based Systems 21 chieve their goals. This paper follows Professor Coopers guideline by detecting the probabilities of customers'satisfaction levels beforehand. [281 P. M Murphy, D.W.Aha, Ud Repository machine learning databases 1996, University Such an approach gives the basis for marketers to adopt various marketing [(29) D.-H. Park, ). Lee, ewoM overload and its effect on consumer behavioral intention strategies to achieve high satisfaction levels. We attribute our recommen- depending on consumer involvement, Electronic Commerce Research and Applica- dation system, with the ultimate goal of marketing online products to ons7(4)(2008)386-398 maximize customer satisfaction, to Dr Coopers pioneering thinking. [301 J.R Quinlan, C4.5: programs for machine learning, Morgan: Morgan Kaufmann [31]. Rak, L Kurgan, M. Reformat, A tree-projection-based algorithm for multi-label Acknowledgements 61 ociative-classification rule generation, Data Knowledge 2008)171-197 The authors thank the editors and tw [32]S Senecal, J Nantel, The influence of online product recommendations on con- 59-169 y the Nationa3b面自m35(2 orm feature selection, and the Program of National Natural Science of China(Project No. 70631003). [34 T Sueyoshi, G.R. Tadiparthi, Agent-Based Approach to U.S. Wholesale Power Trading. IEEE Transactions on Power System 22(2)(2007) [35 E.Thabtah, Areview of associative classification mining, The Knowledge Engineering References Review22(1)(2007)37-65 [36 FA Thabtah, P.L. Cowling, A greedy classification algorithm based on association ule, Applied Soft Computing 7(3) ionrules in large databases, [37] F. Thabtah, P. Cowling, S Hammoud, Improving rule sorting, predictive accuracy 了6 n very Large Data Bases, Sant山114 tional Cor and training time in associative classification, Expert Systems with Applications 31 [2] ANsa met recommendation systems, Journal of Marketing (2006)414-426 [38 F.H. Wang. H M Shao, Effective personalized recommendation based on time [3] A.V. Bodapati, Recommendation systems with purchase data, Journal of Marketing tion clustering and association mining, Expert Systems with Applica- Research (MR 45(1)(2008)77-93 cions27(3)(2004)365-377customers' characteristics to achieve high satisfaction levels. There￾fore, the validity of customers' needs and preferences has an impor￾tant implication on the effectiveness of the recommendation system. Oftentimes consumers do not have clear needs and preferences. There￾fore, finding an effective way to facilitate customers to express their true needs and preferences is essential for the recommendation systems. 6. Epilogue Professor W. W. Cooper, a pioneer researcher in management, has made a significant impact on the fields of decision sciences, oper￾ational research, accounting, marketing, and human resource man￾agement. Among his contributions, Professor Cooper has paid much attention to the research in the area of marketing. He developed in￾novative models to optimize resource allocation for alternative media advertising [5]. In the 1960s, he and his associates built a strategic decision model, DEMON, for marketing new products [6,7]. His idea of creating a decision support system to aid with marketing decision making inspires our pursuit of this research. Information technologies, especially Internet technology, bring significant influence to the traditional marketing environment and changes in the direction of research. As early as 1985, Cooper and his colleagues [8] realized the importance of information technology to marketing research. They argued that researchers and practitioners should handle the “problems that may arise for the relations between marketing management and marketing research because of the rapidly increasing use of personal computers.” Indeed, as the Internet becomes a main part of modern society and online shopping develops into a daily activity, online recommendation systems become ubiquitous and widely utilized by practitioners to improve their revenues. Our research focuses directly on the improvement of recommendation systems. In investigating the rating classification problem, we follow Dr. Cooper's insights about marketing research. In his opinion, when deal￾ing with decision-making problems under uncertainty, the marketing model should be “simple and intuitive, and easy to understand by both academic researchers and practitioners.” Our research proposed a novel associative classification model to handle the rating classification problem. The proposed model is easy to understand, capable of dealing with uncertainty, and more practical and logical than existing techniques. Therefore, the associative classification model can be understood and used by practitioners straightforwardly. Moreover, the outcome of our research is not limited to only the classification results. According to Dr. Cooper, “simply predicting what will happen in the future is of less interest to managers than knowing what has to be changed, and by how much, to achieve their goals.” This paper follows Professor Cooper's guideline by detecting the probabilities of customers' satisfaction levels beforehand. Such an approach gives the basis for marketers to adopt various marketing strategies to achieve high satisfaction levels. We attribute our recommen￾dation system, with the ultimate goal of marketing online products to maximize customer satisfaction, to Dr. Cooper's pioneering thinking. Acknowledgements The authors thank the editors and two anonymous reviewers for their insightful comments. This work was supported by the National Science Foundation of China (Project No.70672097) and the State Key Program of National Natural Science of China (Project No.70631003). References [1] R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, 20th International Conference on Very Large Data Bases, Santiago, 1994, 1994. [2] A. Ansari, S. Essegaier, R. Kohli, Internet recommendation systems, Journal ofMarketing Research (JMR 37 (3) (2000) 363–375. [3] A.V. Bodapati, Recommendation systems with purchase data, Journal of Marketing Research (JMR 45 (1) (2008) 77–93. [4] D.S. Broomhead, D. Lowe, Multivariable functional interpolation and adaptive net￾works, Complex Systems 2 (1988) 321–355. [5] A. Charnes, W.W. Cooper, A constrained game formulation of advertising strategies, Econometrica 22 (3) (1954) 511–512. [6] A. Charnes, W.W. Cooper, J.K. 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