Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Optimization of Multiple Campaigns Reflecting Multiple Recommendation Issue

중복 추천 문제를 반영한 다중 캠페인의 최적화

  • 김용혁 (서울대학교 기계항공공학부) ;
  • 문병로 (서울대학교 컴퓨터공학부)
  • Published : 2005.05.01
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Abstract

In personalized marketing, it is important to maximize customer satisfaction and marketing efficiency. As personalized campaigns are frequently performed, several campaigns are frequently run simultaneously. The multiple recommendation problem occurs when we perform several personalized campaigns simultaneously. This implies that some customers may be bombarded with a considerable number of campaigns. We raise this issue and formulate the multi-campaign assignment problem to solve the issue. We propose dynamic programming method and various heuristic algorithms for solving the problem. With field data, we also present experimental results to verify the importance of the problem formulation and the effectiveness of the proposed algorithms.

개인화된 마케팅에서 고객 만족과 마케팅 효율을 최대화하는 것은 중요하다. 개인화된 캠페인이 수행됨에 따라 여러 캠페인이 동시에 수행되곤 한다. 이 논문에서 우리는 동시에 여러 개인화된 캠페인을 수행할 때 발생하는 중복 추천 문제를 제기한다. 이는 특정 고객에게 상당히 많은 양의 캠페인이 쏟아지게 되는 문제를 말한다. 이 이슈를 해결하기 위한 다중캠페인 할당 문제를 모델링 한다. 그리고 이 문제의 해결 방법으로 동적계획법을 비롯한 여러 휴리스틱 알고리즘들을 제안한다. 필드 데이타의 실험을 통해 제기된 문제 모델의 중요성과 제안된 알고리즘의 효율성을 입증한다.

Keywords

References

  1. R. Dewan, B. Jing, and A. Seidmann. One-to-one marketing on the internet. In Proceedings of the 20th International Conference on Information Systems, pages 93-102, 1999
  2. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61-70, 1992 https://doi.org/10.1145/138859.138867
  3. P. Resnick, N. Iacovou, M. Sushak, P. Bergstrom, and J. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the Computer Supported Collaborative Work Conference, pages 175-186, 1994 https://doi.org/10.1145/192844.192905
  4. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988
  5. C. Feustel and L. Shapiro. The nearest neighbor problem in an abstract metric space. Pattern Recognition Letters, 1:125-128, 1982 https://doi.org/10.1016/0167-8655(82)90025-3
  6. M-S Chen, J. Han, and Philip S. Yu, 'Data Mining : An Overview from a Database Perspective,' IEEE Transactions on Knowledge and Data Engineering, 8(6) : pp.866-883, 1996 https://doi.org/10.1109/69.553155
  7. M. Goebel and L. Gruenwald. A survey of data mining and knowledge discovery software tools. SIGKDD Explorations, 1:20-33, 1999 https://doi.org/10.1145/846170.846172
  8. M. J. A. Berry and G. Linoff. Data Mining Techniques for Marketing, Sales, and Customer Support. John Wiley & Sons, Inc, 1997
  9. J. Herlocker, J. Konstan, A. Borchers, and J. Riedl, 'An Algorithmic Framework for Performing Collaborative Filtering,' In Proceedings of ACM SiGIR-99, 1999 https://doi.org/10.1145/312624.312682
  10. J. Konstan, B. Millr, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl, 'GroupLens: Applying Collaborative Filtering to Usenet News,' Communications of the ACM, Vol.40, No.3, pp.77-87, 1997 https://doi.org/10.1145/245108.245126
  11. C. C. Aggarwal, J. L. Wolf, K. L. Wu, and P. S. Yu. Horting hatches an egg: A new graphtheoretic approach to collaborative filtering. In Knowledge Discovery and Data Mining, pages 201-212, 1999
  12. D. Greening. Building consumer trust with accurate product recommendations. Technical Report LMWSWP-210-6966, LikeMinds White Paper, 1997
  13. Upendra S. and Patti M,. 'Social Information Filtering: Algorithms for Automating 'Word of Mouth',' Proc. of ACM CHI'95 Conference on Human Factors in Computing Systems, pp. 210-217, 1995 https://doi.org/10.1145/223904.223931
  14. J. Schafer, J. Konstan, and J. Riedl, 'Recommender System in E-Commerce,' Proceedings of the ACM Conference on Electronic Commerce, 1999 https://doi.org/10.1145/336992.337035
  15. R. Bellman. Dynamic Programming. Princeton University Press, 1957
  16. S. E. Dreyfus and A. M. Law. The Art and Theory of Dynamic Programming. Academic Press, 1977
  17. G. M. Adel'son-Vel'skii and E. M. Landis. An algorithm for the organization of information. Soviet Mathematics Doklady, 3:1259-1262, 1962
  18. Email Marketing Maximized, Insight Report 2000. Peppers and Rogers Group, 2000
  19. Y. K. Kwon and B. R. Moon. Personalized email marketing with a genetic programming circuit model. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1352-1358, 2001