Detecting adversarial advertisements in the wild

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2011, Pages 274-282

  Sculley, D ^a   Otey, Matthew Eric ^a   Pohl, Michael ^a   Spitznagel, Bridget ^a   Hainsworth, John ^a   Zhou, Yunkai ^a  

^a GOOGLE INC   (United States)

Author keywords

Adversarial learning; Data mining; Online advertisement

Indexed keywords

AUTOMATION; E-LEARNING; KNOWLEDGE MANAGEMENT;

ADVERSARIAL LEARNING; ADVERTISING SYSTEMS; DEPLOYED SYSTEMS; FALSE POSITIVE AND FALSE NEGATIVES; HIGH COSTS; LARGE SCALE DATA; LOW QUALITIES; ONLINE ADVERTISEMENTS; ONLINE ADVERTIZING; SYSTEM USE;

DATA MINING;

EID: 80052672008     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020408.2020455     Document Type: Conference Paper

References (40)

1. J. Attenberg and F. J. Provost. Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance. In KDD, 2010.
2. C. M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag New York, Inc., 2006.
3. D. M. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. JMLR, 3, 2003.
4. L. Bottou and O. Bousquet. The tradeoffs of large scale learning. In Advances in Neural Information Processing Systems 20. 2008.
5. D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. In WWW'08: Proceeding of the 17th international conference on World Wide Web, 2008.
6. N. V. Chawla, N. Japkowicz, and A. Kotcz. Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. Newsl., 6, June 2004.
7. N. Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma. Adversarial classification. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD'04, 2004.
8. J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Commun. ACM, 51, January 2008.
9. S. Deerwester, S. Dumais, T. Landuaer, G. Furnas, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society of Information Science, 1990.
10. J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra. Efficient projections onto the l1-ball for learning in high dimensions. In ICML'08: Proceedings of the 25th international conference on Machine learning, 2008.
11. N. Duffield, C. Lund, and M. Thorup. Priority sampling for estimation of arbitrary subset sums. J. ACM, 54, December 2007.
12. J. Goodman, G. V. Cormack, and D. Heckerman. Spam and the ongoing battle for the inbox. Commun. ACM, 50(2), 2007.
13. Landing page and site policies. Google AdWords Help Center, 2011. http://goo.gl/XcbPO.
14. C.-W. Hsu and C.-J. Lin. A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on, 13(2), Mar. 2002.
15. IAB internet advertising revenue report, 2010. http://www.iab.net/media/ file/IAB-report-1H-2010-Final.pdf.
16. T. Joachims. Making large-scale support vector machine learning practical. 1999.
17. T. Joachims. Optimizing search engines using clickthrough data. In KDD'02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002.
18. T. Joachims. A support vector method for multivariate performance measures. In ICML'05: Proceedings of the 22nd international conference on Machine learning, 2005.
19. J. Langford. Vowpal wabbit. Open source release, 2007. http://hunch.net/~vw/.
20. J. Langford, L. Li, and T. Zhang. Sparse online learning via truncated gradient. J. Mach. Learn. Res., 10, 2009.
21. D. D. Lewis, Y. Yang, T. G. Rose, and F. Li. RCV1: A new benchmark collection for text categorization research. J. Mach. Learn. Res., 5, 2004.
22. D. Lowd and C. Meek. Adversarial learning. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, KDD'05, 2005.
23. G. Mann, R. McDonald, M. Mohri, N. Silberman, and D. Walker. Efficient large-scale distributed training of conditional maximum entropy models. In Advances in Neural Information Processing Systems 22. 2009.
24. H. B. McMahan and M. Streeter. Tighter bounds for multi-armed bandits with expert advice. In COLT'09: 22nd Annual Conference on Learning Theory, 2009.
25. C.-E. Särndal, B. Swensson, and J. Wretman. Model Assisted Survey Sampling. Springer, 2003.
26. D. Sculley. Large scale learning to rank. In NIPS 2009 Workshop on Advances in Ranking, 2009.
27. D. Sculley, R. G. Malkin, S. Basu, and R. J. Bayardo. Predicting bounce rates in sponsored search advertisements. In KDD'09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009.
28. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In ICML'07: Proceedings of the 24th international conference on Machine learning, 2007.
29. R. Snow, B. O'Connor, D. Jurafsky, and A. Y. Ng. Cheap and fast|but is it good?: evaluating non-expert annotations for natural language tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP'08, 2008.
30. S. Sonnenburg, G. Rätsch, and B. Schölkopf. Large scale genomic sequence svm classifiers. In ICML'05: Proceedings of the 22nd international conference on Machine learning, 2005.
31. M. Szegedy. The DLT priority sampling is essentially optimal. In Proceedings of the thirty-eighth annual ACM symposium on Theory of computing, STOC'06, 2006.
32. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res., 2, March 2002.
33. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. CVPR: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1:511, 2001.
34. L. von Ahn, B. Maurer, C. McMillen, D. Abraham, and M. Blum. reCAPTCHA: Human-based character recognition via web security measure. September 2008.
35. M. Weimer, S. Rao, and M. Zinkevich. A convenient framework for efficient parallel multipass algorithms. In NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds, 2010.
36. K. Weinberger, A. Dasgupta, J. Langford, A. Smola, and J. Attenberg. Feature hashing for large scale multitask learning. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML'09, 2009.
37. W. Yih, J. Goodman, and G. Hulten. Learning at low false positive rates. In Proceedings of the Third Conference on Email and Anti-Spam (CEAS), 2006.
38. T. Zhang. Solving large scale linear prediction problems using stochastic gradient descent algorithms. In ICML'04: Proceedings of the twenty-first international conference on Machine learning, 2004.
39. M. Zinkevich, A. Smola, and J. Langford. Slow learners are fast. In Advances in Neural Information Processing Systems 22. 2009.
40. M. Zinkevich, M. Weimer, A. Smola, and L. Li. Parallelized stochastic gradient descent. In Advances in Neural Information Processing Systems 23. 2010.