Data acquisition and cost-effective predictive modeling: Targeting offers for electronic commerce

ACM International Conference Proceeding Series

Volumn 258, Issue , 2007, Pages 389-398

  Provost, Foster ^a   Melville, Prem ^b   Saar Tsechansky, Maytal ^c  

^a NEW YORK UNIVERSITY   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

^c UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Active feature value acquisition; Active information acquisition; Active learning; Electronic commerce; Supervised learning

Indexed keywords

ACTIVE FEATURE-VALUE ACQUISITION; ACTIVE INFORMATION ACQUISITION; ACTIVE LEARNING;

DATA ACQUISITION; DATA REDUCTION; INFORMATION RETRIEVAL; MATHEMATICAL MODELS; SUPERVISED LEARNING;

ELECTRONIC COMMERCE;

EID: 36849030268     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1282100.1282172     Document Type: Conference Paper

References (61)

1. N. Abe and H. Mamitsuka. Query learning strategies using boosting and bagging. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), pages 1-10, 1998.
2. S. Ansari, R. Kohavi, L. Mason, and Z. Zheng. Integrating e-commerce and data mining: Architecture and challenges. In Proceedings of WEBKDD, 2000.
3. A. C. Bemmaor. Predicting behavior from intention-to-buy measures: The parametric case. Journal of Marketing Research (JMR), 32(2), May 1995.
4. M. Bilgic and L. Getoor. Voila: Efficient feature-value acquisition for classification. In AAAI '07: Proceedings of the 22nd National Conference on Artificial Intelligence, July 2007.
5. K. Brinker. Incorporating diversity in active learning with support vector machines. In Proceedings of 20th International Conference on Machine Learning (ICML-2003), pages 59-66. AAAI Press, 2003.
6. D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
7. I. Dagan and S. P. Engelson. Committee-based sampling for training probabilistic classifiers. In Proceedings of the Twelfth International Conference on Machine Learning (ICML-95), pages 150-157, San Francisco, CA, 1995. Morgan Kaufmann.
8. H. Dai, L. Zhao, Z. Nie, J. Wen, L. Wang, and Y. Li. Detecting online commercial intention (OCI). In Proceedings of the 15th International World Wide Web Conference (WWW-06), 2006.
9. D. T. Davis and J. N. Hwang. Attentional focus training by boundary region data selection. In International Joint Conference on Neural Networks, volume 1, pages 676-81. IEEE, 1992.
10. D. Dobkin, D. Gunopoulos, and S. Kasif. Computing optimal shallow decision trees. In International Workshop on Mathematics and Artificial Intelligence, 1996.
11. P. Domingos and M. Richardson. Mining the network value of customers. In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining, pages 57-66, 2001.
12. V. V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, NY, 1972.
13. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28:133-168, 1997.
14. R. Gilad-Bachrach, A. Navot, and N. Tishby. Query by committee made real. In Advances in Neural Information Processing Systems, 2005.
15. R. Greiner, A. Grove, and D. Roth. Learning cost-sensitive active classifiers. Artificial Intelligence (AIJ), 139(2):137-174, 2002.
16. P. Haase, M. Ehrig, A. Hotho, and B. Schnizler. Personalized information access in a bibliographic peer-to-peer system. In Proceedings of the AAAI Workshop on Semantic Web Personalization, 2004.
17. S. Hill, F. Provost, and C. Volinsky. Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 22(2), May 2006.
18. V. S. Iyengar, C. Apte, and T. Zhang. Active learning using adaptive resampling. In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000), pages 91-98, 2002.
19. L. P. Kaelbling, M. L. Littman, and A. P. Moore. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4:237-285, 1996.
20. A. Kapoor and R. Greiner. Learning and classifying under hard budgets. In Proceedings of the European Conference on Machine Learning (ECML-05), Porto, Portugal, October 2005.
21. K. L. Keller. Conceptualizing, measuring, managing customer-based brand equity. Journal of Marketing, 57(1):11-22, 1993.
22. J. Kiefer. Optimal experimental designs. J. R. Stat. Soc., series B 21:272-304, 1959.
23. R. Kohavi, R. Henne, and D. Sommerfield. Practical guide to controlled experiments on the web: Listen to your customers not to the HiPPO. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2007), 2007.
24. R. Kohavi and F. Provost. Applications of data mining to electronic commerce. Data Mining and Knowledge Discovery, 5(1/2), 2001.
25. S. Kullbak and R. Leibler. On information and sufficiency. Annals of Mathematical Statistics, 22(1), 1951.
26. D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proceedings of Seventeenth International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-94), 1994.
27. D. D. Lewis and J. Catlett. Heterogeneous uncertainty sampling for supervised learning. In Proceedings of the Eleventh International Conference on Machine Learning (ICML-94), pages 148-156, San Francisco, CA, July 1994. Morgan Kaufmann.
28. H. Lieberman and T. Selker. Out of context: Computer systems that adapt to, and learn from, context. IBM Systems Journal, 39, 2000.
29. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, page 76U80, 2003.
30. M. Lindenbaum, S. Markovitch, and D. Rusakov. Selective sampling for nearest neighbor classifiers. In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pages 366-371, 1999.
31. D. Lizotte, O. Madani, and R. Greiner. Budgeted learning of naive-Bayes classifiers. In Proceedings of 19th Conference on Uncertainty in Artificial Intelligence (UAI-2003), Acapulco, Mexico, 2003.
32. P. Manchanda, J.-P. Dub, K. Y. Goh, and P. K. Chintagunta. The effect of banner advertising on internet purchasing. Journal of Marketing Research (JMR), 43(1):98-108, Feb 2006.
33. P. Melville and R. J. Mooney. Diverse ensembles for active learning. In Proceedings of 21st International Conference on Machine Learning (ICML-2004), pages 584-591, Banff, Canada, July 2004.
34. P. Melville, M. Saar-Tsechansky, F. Provost, and R. Mooney. Active feature-value acquisition for classifier induction. In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM-04), pages 483-486, 2004.
35. P. Melville, M. Saar-Tsechansky, F. Provost, and R. Mooney. An expected utility approach to active feature-value acquisition. In Proceedings of the International Conference on Data Mining, pages 745-748, Houston, TX, November 2005.
36. P. Melville, S. M. Yang, M. Saar-Tsechansky, and R. Mooney. Active learning for probability estimation using Jensen-Shannon divergence. In Proceedings of the European Conference on Machine Learning (ECML-05), pages 268-279, Porto, Portugal, October 2005.
37. T. Mitchell. Machine Learning. McGraw-Hill, New York, NY, 1997.
38. B. Mobasher and S. S. Anand. Intelligent techniques for web personalization. In Lecture Notes in Artificial Intelligence (LNAI 3169). Springer, 2005.
39. W. W. Moe. Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1/2), 2003.
40. O. Nasraoui, C. Rojas, and C. Cardona. A framework for mining evolving trends in web data streams using dynamic learning and retrospective validation. Journal of Computer Networks- Special Issue on Web Dynamics, 50(10):1425-1652, July 2006.
41. B. Padmanabhan, Z. Zheng, and S. O. Kimbrough. Personalization from incomplete data: what you don't know can hurt. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), pages 154-163, 2001.
42. E. Pednault, N. Abe, and B. Zadrozny. Sequential cost-sensitive decision making with reinforcement learning. In Eigth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2002), 2002.
43. F. Provost. Toward economic machine learning and utility-based data mining. Invited talk at the KDD-05 Workshop on Utility-Based Data Mining: http://storm.cis.fordham.edu/gweiss/ubdm05/Provostslides.pdf.
44. N. V. Raman and J. D. Leckenby. Factors affecting consumers' Webad visits. European Journal of Marketing, 32(7/8), 1998.
45. M. Regelson and D. Fain. Predicting click-through rate using keyword clusters. In Proceedings of the 2nd Workshop on Sponsored Search Auctions, 2006.
46. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: Estimating the click-through rate for new ads. In Proceedings of the 16th International World Wide Web Conference (WWW-07), 2007.
47. H. Robbins. Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, 55, 1952.
48. S. Rosset, E. Neumann, U. Eick, and N. Vatnik. Customer lifetime value models for decision support. Data Mining and Knowledge Discovery, 7(3):321-339, 2003.
49. N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In Proceedings of 18th International Conference on Machine Learning (ICML-2001), pages 441-448. Morgan Kaufmann, San Francisco, CA, 2001.
50. M. Saar-Tsechansky, P. Melville, and F. Provost. Active feature-value acquisition. Technical report, McCombs Research Paper Series No. IROM-08-06, 2006.
51. M. Saar-Tsechansky and F. Provost. Handling missing values when applying classification models. Journal of Machine Learning Research. Forthcoming.
52. M. Saar-Tsechansky and F. Provost. Active sampling for class probability estimation and ranking. Machine Learning, 54:153-178, 2004.
53. M. Saar-Tsechansky and F. Provost. Decision-centric active learning of binary-outcome models. Information Systems Research, 18(1), 2007.
54. D. R. Self and R. F. Lusch. Direct response marketing: a comparative review. Journal of Marketing, 50(1), 1986.
55. H. S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the ACM Workshop on Computational Learning Theory, Pittsburgh, PA, 1992.
56. V. Sheng and C. Ling. Feature value acquisition in testing: A sequential batch test algorithm. In Proceedings of 2006 International Conference on Machine Learning (ICML 2006), pages 809-816, 2006.
57. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
58. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000), 2000.
59. P. Turney. Types of cost in inductive concept learning. In Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning (WCSL at ICML-2000), 2000.
60. W. Yih, J. Goodman, and V. R. Carvalho. Finding advertising keywords on web pages. In Proceedings of the 15th International World Wide Web Conference (WWW-06), 2006.
61. Z. Zheng and B. Padmanabhan. Selectively acquiring customer information: A new data acquisition problem and an active learning based solution. Management Science, 52(5):697-712, 2006.