Functional bid landscape forecasting for display advertising

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9851 LNAI, Issue , 2016, Pages 115-131

  Wang, Yuchen ^a   Ren, Kan ^a   Zhang, Weinan ^a   Wang, Jun ^b   Yu, Yong ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMMERCE; COMPETITION; COSTS; DECISION TREES; LEARNING SYSTEMS; MARKETING;

CATEGORICAL FEATURES; DECISION TREE MODELING; DISPLAY ADVERTISINGS; FORECASTING METHODS; FUNCTION MAPPING; MARKET COMPETITION; ONLINE ADVERTISING; TRADING MECHANISM;

FORECASTING;

EID: 84988664536     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46128-1_8     Document Type: Conference Paper

References (22)

1. Agarwal, D., Ghosh, S., Wei, K., You, S.: Budget pacing for targeted online advertisements at linkedin. In: KDD (2014)
2. Amin, K., Kearns, M., Key, P., Schwaighofer, A.: Budget optimization for sponsored search: Censored learning in mdps. arXiv preprint arXiv:1210.4847 (2012)
3. Cui, Y., Zhang, R., Li, W., Mao, J.: Bid landscape forecasting in online ad exchange marketplace. In: KDD (2011)
4. Edelman, B., Ostrovsky, M., Schwarz, M.: Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. Technical report, National Bureau of Economic Research (2005)
5. Faddoul, J.B., Chidlovskii, B., Gilleron, R., Torre, F.: Learning multiple tasks with boosted decision trees. In: Flach, P.A., Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), pp. 681–696. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33460-349
6. García-Laencina, P.J., Sancho-Gómez, J.L., Figueiras-Vidal, A.R.: Pattern classification with missing data: a review. Neural Comput. Appl. 19(2), 263–282 (2010)
7. Google: The arrival of real-time bidding (2011)
8. Johnson, N.L.: Survival Models and Data Analysis. Wiley, New York (1999)
9. Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53, 457–481 (1958)
10. Kullback, S.: Letter to the editor: the kullback-leibler distance (1987)
11. Lee, K.C., Jalali, A., Dasdan, A.: Real time bid optimization with smooth budget delivery in online advertising. In: ADKDD (2013)
12. Lee, K.C., Orten, B.B., Dasdan, A., Li, W.: Estimating conversion rate in display advertising from past performance data (2012)
13. Liao, H., Peng, L., Liu, Z., Shen, X.: ipinyou global rtb bidding algorithm competition dataset. In: ADKDD (2014)
14. Marlin, B.M., Zemel, R.S.: Collaborative prediction and ranking with non-random missing data. In: RecSys (2009)
15. Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: ICDM (2008)
16. Perlich, C., Dalessandro, B., Hook, R., Stitelman, O., Raeder, T., Provost, F.: Bid optimizing and inventory scoring in targeted online advertising. In: KDD (2012)
17. Stojanova, D., Ceci, M., Appice, A., Džeroski, S.: Network regression with predictive clustering trees. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), pp. 333–348. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23808-622
18. Wu, W.C.H., Yeh, M.Y., Chen, M.S.: Predicting winning price in real time bidding with censored data. In: KDD (2015)
19. Yuan, S., Wang, J., Chen, B., Mason, P., Seljan, S.: An empirical study of reserve price optimisation in real-time bidding. In: KDD (2014)
20. Yuan, S., Wang, J., Zhao, X.: Real-time bidding for online advertising: measurement and analysis. In: ADKDD (2013)
21. Zhang, W., Wang, J.: Statistical arbitrage mining for display advertising. In: KDD (2015)
22. Zhang, W., Yuan, S., Wang, J.: Optimal real-time bidding for display advertising. In: KDD (2014)