Supporting Smart Interactions with Predictive Analytics

  • Patrick Martin
  • Marie Matheson
  • Jimmy Lo
  • Joanna Ng
  • Daisy Tan
  • Brian Thomson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6400)

Abstract

Smart interactions, where web services are configured and integrated across multiple servers in order to better address the needs of the user, will be much more user-centric and responsive to user needs than current interactions. However, Smart interactions associated with decision-making tasks will specifically have to provide enhanced information or guidance linked to that task. In this paper we examine how predictive analytics can be used to provide cognitive support for smart interactions and outline a method consistent with the smart internet user model to facilitate the creation of predictive analytics components or services to support smart interactions for decision-making tasks.

Keywords

Smart internet predictive analytics data warehouse 

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References

  1. 1.
    Ng, J.W., Chignell, M., Cordy, J.R.: The Smart Internet: Transforming the Web for the User. In: Martin, P., Kark, A.W., Stewart, D. (eds.) Proceedings of the 2009 Conference of the Centres for Advanced Studies on Collaborative Research, CASCON 2009, Ontario, Canada, November 02-05, pp. 285–296. ACM, New York (2009)CrossRefGoogle Scholar
  2. 2.
    Agosta, L.: The Future of Data Mining – Predictive Analytics. DM Review (2004)Google Scholar
  3. 3.
    Bigus, J., Chitnis, U., Deshpande, P., Kannan, R., Mohania, M., Negi, S., Deepak, P., Pednault, E., Soni, S., Telkar, B., White, B.: CRM Analytics Framework. In: Proc. of 15th Int. Conf. on Management of Data (COMAD 2009), Mysore, India (2009)Google Scholar
  4. 4.
    Tung, L., Xu, Y.: A framework for e-commerce oriented recommendation systems, Active Media Technology. In: Proceedings of the International Conference on AMT 2005, May 19-21, pp. 309–314 (2005)Google Scholar
  5. 5.
    Chuang, H., Wang, L., Pan, C.: A Study on the Comparison between Content-Based and Preference-Based Recommendation Systems, Semantics, Knowledge and Grid. In: Fourth International Conference on SKG 2008, December 3-5, pp. 477–480 (2008)Google Scholar
  6. 6.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, EC 2000, Minneapolis, Minnesota, United States, October 17-20, pp. 158–167. ACM, New York (2000)Google Scholar
  7. 7.
    Apte, C., Bibelnieks, E., Natarajan, R., Pednault, E., Tipu, F., Campbell, D., Nelson, B.: Segmentation-based modeling for advanced targeted marketing. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, pp. 408–413 (2001)Google Scholar
  8. 8.
    Apte, C.V., Hong, S.J., Natarajan, R., Pednault, E.P., Tipu, F.A., Weiss, S.M.: Data-intensive analytics for predictive modeling. IBM J. Res. Dev. 47(1), 17–23 (2003)CrossRefGoogle Scholar
  9. 9.
    Abe, N., Pednault, E., Wang, H., Zadrozny, B., Fan, W., Apte, C.: Empirical Comparison of Various Reinforcement Learning Strategies for Sequential Targeted Marketing. In: Proceedings of the 2002 IEEE international Conference on Data Mining. IEEE Computer Society, Washington (2002)Google Scholar
  10. 10.
    Masnadi-Shirazi, H., Vasconcelos, N.: Cost-sensitive boosting. IEEE Transactions on PP Pattern Analysis and Machine Intelligence 99, 1 (2010)Google Scholar
  11. 11.
    Kaelbling, L.P.: Reinforcement learning: A survey. Journal of artificial intelligence research 4, 237–285 (1996)Google Scholar
  12. 12.
    Sutton, R.S.: Learning to predict by the method of temporal differences. Machine Learning 3(1), 9–44 (1988)Google Scholar
  13. 13.
    Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuron-like adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics, SMC 13(5), 834–846 (1983)CrossRefGoogle Scholar
  14. 14.
    Watkins, C.J.C.H.: Learning from Delayed Rewards. Ph.D. thesis, King’s College, Cambridge, UK (1989)Google Scholar
  15. 15.
    Watkins, C.J.C.H., Dayan, P.: Q-learning. Machine Learning 8(3), 279–292 (1992)MATHGoogle Scholar
  16. 16.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Comm. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  17. 17.
    Hong, S.J., Natarajan, R., Belitskaya, I.: A New Approach for Item Choice Recommendations. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 131–140. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  18. 18.
    Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Proc. of the SIGIR Workshop on Recommender Systems, Berkeley CA (1999)Google Scholar
  19. 19.
    Papamichail, G.P., Papamichail, D.P.: The k-means range algorithm for personalized data clustering in e-commerce. European Journal of Operational Research 177(3), 1400–1408 (2007)CrossRefMATHGoogle Scholar
  20. 20.
    Huang, Y.: An item based collaborative filtering using item clustering prediction. In: ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009, vol. 4, pp. 54–56 (2009)Google Scholar
  21. 21.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Patrick Martin
    • 1
  • Marie Matheson
    • 1
  • Jimmy Lo
    • 2
  • Joanna Ng
    • 2
  • Daisy Tan
    • 2
  • Brian Thomson
    • 2
  1. 1.School of ComputingQueen’s UniversityCanada
  2. 2.Toronto LaboratoryIBM CanadaCanada

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