Bid landscape forecasting in online Ad exchange marketplace

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

Volumn , Issue , 2011, Pages 265-273

  Cui, Ying ^a   Zhang, Ruofei ^a   Li, Wei ^a   Mao, Jianchang ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

Ad exchange marketplace; Bid landscape forecasting; Online display advertising

Indexed keywords

DATA MINING; DECISION TREES; ELECTRONIC COMMERCE; FORESTRY; MARKETING;

A-SPOTS; AD EXCHANGE MARKETPLACE; AD EXCHANGES; BID LANDSCAPE FORECASTING; BUSINESS MODELS; DISPLAY ADVERTISINGS; GUARANTEED DELIVERY; ONLINE ADVERTIZING; ONLINE DISPLAY ADVERTIZING; SPOT MARKET;

FORECASTING;

EID: 80052658695     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2020408.2020454     Document Type: Conference Paper

References (14)

1. D. Agarwal, A. Z. Broder, D. Chakrabarti, D. Diklic, V. Josifovski, and M. Sayyadian. Estimating rates of rare events at multiple resolutions. In KDD'07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 16-25, New York, NY, USA, 2007. ACM. (Pubitemid 350229189)
2. C. Cardie. Using decision trees to improve case-based learning. In In Proceedings of the Tenth International Conference on Machine Learning, pages 25-32. Morgan Kaufmann, 1993.
3. A. P. Dempster, N. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39:1-38, 1977.
4. J. H. Friedman. Stochastic gradient boosting. Computational Statistics and Data Analysis, 38:367-378, 1999. (Pubitemid 34197167)
5. J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189-1232, 2000. (Pubitemid 33405972)
6. A. Ghosh, B. I. Rubinstein, S. Vassilvitskii, and M. Zinkevich. Adaptive bidding for display advertising. In WWW'09: Proceedings of the 18th international conference on World wide web, pages 251-260, New York, NY, USA, 2009. ACM.
7. T. Hastie, R. Tibshirani, and J. H. Friedman. Chapter 10. boosting and additive trees. In The elements of statistical learning: data mining, inference, and prediction, pages 337-384. New York: Springer, 2009.
8. K. Kira and L. A. Rendell. A practical approach to feature selection. In ML'92: Proceedings of the Ninth International Workshop on Machine Learning, pages 249-256, San Francisco, CA, USA, 1992. Morgan Kaufmann Publishers Inc.
9. G. J. McLachlan and D. Peel. Finite Mixture Models. New York: Wiley, 2000.
10. K. Media. U.S. Advertising Expenditures Declined 12.3 Percent in 2009, 2009. http://www.businesswire.com/news/home/20100317005458/en/Kantar-Media- Reports-U.S.-Advertising-Expenditures-Declined.
11. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In WWW'07: Proceedings of the 16th international conference on World Wide Web, pages 521-530, New York, NY, USA, 2007. ACM. (Pubitemid 47582281)
12. X. Wang, A. Broder, M. Fontoura, and V. Josifovski. A search-based method for forecasting ad impression in contextual advertising. In WWW'09: Proceedings of the 18th international conference on World wide web, pages 491-500, New York, NY, USA, 2009. ACM.
13. J. Yang, E. Vee, S. Vassilvitskii, J. Tomlin, J. Shanmugasundaram, T. Anastasakos, and O. Kennedy. Inventory allocation for online graphical display advertising. Technical Report YL-2010-004, Yahoo! Labs., aug 2010.
14. L. Yu and H. Liu. Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), pages 856-863, 2003.