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Multicampaign Assignment Problem
March 2006 (vol. 18 no. 3)
pp. 405-414
It is crucial to maximize targeting efficiency and customer satisfaction in personalized marketing. State-of-the-art techniques for targeting focus on the optimization of individual campaigns. Our motivation is the belief that the effectiveness of a campaign with respect to a customer is affected by how many precedent campaigns have been recently delivered to the customer. We raise the multiple recommendation problem, which occurs when performing several personalized campaigns simultaneously. We formulate the multicampaign assignment problem to solve this issue and propose algorithms for the problem. The algorithms include dynamic programming and efficient heuristic methods. We verify by experiments the effectiveness of the problem formulation and the proposed algorithms.

[1] E-mail Marketing Maximized, Insight Report 2000. Peppers and Rogers Group, 2000.
[2] “The Epiphany E6 Architecture J2EE, Open Standards-Based CRM,” technical white paper, Epiphany, Nov. 2004.
[3] G.M. Adel'son-Vel'skii and E.M. Landis, “An Algorithm for the Organization of Information,” Soviet Math. Doklady, vol. 3, pp. 1259-1262, 1962.
[4] C.C. Aggarwal, J.L. Wolf, K.L. Wu, and P.S. Yu, “Horting Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering,” Proc. Knowledge Discovery and Data Mining Conf., pp. 201-212, 1999.
[5] R. Bellman, Dynamic Programming. Princeton Univ. Press, 1957.
[6] M.J.A. Berry and G. Linoff, Data Mining Techniques for Marketing, Sales, and Customer Support. John Wiley and Sons, 1997.
[7] M.J.A. Berry and G. Linoff, Mastering Data Mining, the Art and Science of Customer Relationship Management. John Wiley and Sons, 2000.
[8] E. Cela, The Quadratic Assignment Problem: Theory and Applications. Kluwer Academic, 1998.
[9] M.S. Chen, P.S. Han, and J. Yu, “Data Mining: An Overview from a Database Perspective,” IEEE Trans. Knowledge and Data Eng., vol. 8, no. 6, pp. 866-883, Dec. 1996.
[10] R. Dewan, B. Jing, and A. Seidmann, “One-to-One Marketing on the Internet,” Proc. 20th Int'l Conf. Information Systems, pp. 93-102, 1999.
[11] S.E. Dreyfus and A.M. Law, The Art and Theory of Dynamic Programming. Academic Press, 1977.
[12] J. Dyché, The CRM Handbook: A Business Guide to Customer Relationship Management. Addison-Wesley, 2001.
[13] C. Feustel and L. Shapiro, “The Nearest Neighbor Problem in an Abstract Metric Space,” Pattern Recognition Letters, vol. 1, pp. 125-128, 1982.
[14] M. Goebel and L. Gruenwald, “A Survey of Data Mining and Knowledge Discovery Software Tools,” SIGKDD Explorations, vol. 1, pp. 20-33, 1999.
[15] D. Goldberg, D. Nichols, B.M. Oki, and D. Terry, “Using Collaborative Filtering to Weave an Information Tapestry,” Comm. ACM, vol. 35, no. 12, pp. 61-70, 1992.
[16] D. Greening, “Building Consumer Trust with Accurate Product Recommendations,” Technical Report LMWSWP-210-6966, LikeMinds white paper, 1997.
[17] J.L. Herlocker, J.A. Konstan, A. Borchers, and J. Riedl, “An Algorithmic Framework for Performing Collaborative Filtering,” Proc. 22nd Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 230-237, 1999.
[18] J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J.T. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
[19] M. Holsheimer and L. Meindertsma, “Data Mining Integrated in CRM,” Informatie, vol. 41, pp. 10-16, 1999.
[20] Z. Huang, H. Chen, and D. Zeng, “Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 116-142, 2004.
[21] Z. Huang, D. Zeng, and H. Chen, “A Comparative Study of Recommendation Algorithms in E-Commerce Applications,” IEEE Intelligent Systems, 2004.
[22] A.K. Jain and R.C. Dubes, Algorithms for Clustering Data. Prentice Hall, 1988.
[23] J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordan, and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Comm. ACM, vol. 40, pp. 77-87, 1997.
[24] H.W. Kuhn, “The Hungarian Method for the Assignment Problem,” Naval Research Logistics Quarterly, vol. 2, pp. 83-97, 1955.
[25] Y.K. Kwon and B.R. Moon, “Personalized E-Mail Marketing with a Genetic Programming Circuit Model,” Proc. Genetic and Evolutionary Computation Conf., pp. 1352-1358, 2001.
[26] B.A.S. Martin, J.V. Durme, M. Raulas, and M. Merisavo, “E-Mail Advertising: Exploratory Insights from Finland,” J. Advertising Research, vol. 43, no. 3, pp. 293-300, 2003.
[27] A. Maurino and P. Fraternali, “Commercial Tools for the Development of Personalized Web Applications: A Survey,” Proc. Third Int'l Conf. E-Commerce and Web Technologies, pp. 99-108, 2002.
[28] L. McCauley and S. Franklin, “A Large-Scale Multiagent System for Navy Personnel Distribution,” Connection Science, vol. 14, no. 4, pp. 371-385, 2002.
[29] B.N. Miller, J.A. Konstan, and J.T. Riedl, “PocketLens: Toward a Personal Recommender System,” ACM Trans. Information Systems, vol. 22, no. 3, pp. 437-476, 2004.
[30] D. Peppers and M. Rogers, The One to One Future. Doubleday and Company, 1996.
[31] P. Resnick, N. Iacovou, M. Sushak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. Computer Supported Collaborative Work Conf., pp. 175-186, 1994.
[32] J.B. Schafer, J.A. Konstan, and J.T. Riedl, “Recommender Systems in E-Commerce,” Proc. ACM Conf. Electronic Commerce, pp. 158-166, 1999.
[33] U. Shardanand and P. Maes, “Social Information Filtering: Algorithms for Automating ‘Word of Mouth’,” Proc. ACM CHI '95 Conf. Human Factors in Computing Systems, vol. 1, pp. 210-217, 1995.

Index Terms:
Personalized marketing, multicampaign assignment, dynamic programming, heuristic algorithms.
Yong-Hyuk Kim, Byung-Ro Moon, "Multicampaign Assignment Problem," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 3, pp. 405-414, March 2006, doi:10.1109/TKDE.2006.49
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