Sequential selection of correlated ads by POMDPs

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 515-524

  Yuan, Shuai ^a   Wang, Jun ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

computational advertising; correlation; pomdps; revenue optimisation; value iteration

Indexed keywords

COLLABORATIVE FILTERING; COMPUTATIONAL ADVERTISINGS; CONTENT-BASED; DATA SETS; MATHEMATICAL DERIVATION; OFFLINE; ONLINE ADVERTISING; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS; POMDPS; REVENUE OPTIMISATION; SEQUENTIAL SELECTION; VALUE ITERATION; WEB USERS;

COMMERCE; ITERATIVE METHODS; KNOWLEDGE MANAGEMENT; MARKETING; OPTICAL CORRELATION; SEARCH ENGINES; STATISTICAL TESTS;

WEBSITES;

EID: 84871072508     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2396828     Document Type: Conference Paper

References (33)

1. The first half '11 internet advertising report. http://goo.gl/NRZ4S, 2011.
2. AdWords, G. Google adwords traffic estimator. http://goo.gl/TYdOQ, 2012.
3. Anagnostopoulos, A., Broder, A., Gabrilovich, E., Josifovski, V., and Riedel, L. Just-in-time contextual advertising. In Proceedings of the ACM-CIKM 07'.
4. Auer, P., Cesa-Bianchi, N., and Fischer, P. Finite-time analysis of the multiarmed bandit problem. Machine learning 47, 2 (2002), 235-256.
5. Bertsekas, D. P. Dynamic Programming and Optimal Control. Athena Scientific, 1995.
6. Bharadwaj, V., Ma, W., Schwarz, M., Shanmugasundaram, J., Vee, E., Xie, J., and Yang, J. Pricing guaranteed contracts in online display advertising. In Proceedings of the ACM-CIKM 10'.
7. Breese, J. S., Heckerman, D., and Kadie, C. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th conference on Uncertainty in artificial intelligence (1998), UAI'98, pp. 43-52.
8. Broder, A., Fontoura, M., Josifovski, V., and Riedel, L. A semantic approach to contextual advertising. In Proceedings of the ACM-SIGIR 07'.
9. Bullas, J. 50 amazing facts and figures about google. http://goo.gl/EfrLS, 2011.
10. Cassandra, A. A survey of POMDP applications. In Working Notes of AAAI 1998 Fall Symposium on Planning with Partially Observable Markov Decision Processes.
11. Chakrabarti, D., Agarwal, D., and Josifovski, V. Contextual advertising by combining relevance with click feedback. In Proceedings of the WWW 08'.
12. Chickering, D., and Heckerman, D. Targeted advertising on the web with inventory management. Interfaces (2003), 71-77.
13. Fjell, K. Online advertising: Pay-per-view versus pay-per-click a comment. Journal of Revenue & Pricing Management 2, 3 (2009), 200-206.
14. Fridgeirsdottir, K., and Asadolahi, S. Revenue management for online advertising: Impatient advertisers. Tech. rep., Working paper, London Business School, 2007.
15. Google White Paper. Profiting from non-guaranteed advertising: The value of dynamic allocation and auction pricing for online publishers, 2012.
16. Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. An algorithmic framework for performing collaborative filtering. In Proceedings of the ACM-SIGIR '99.
17. Hu, Y., Shin, J., and Tang, Z. Pricing of online advertising: Cost-per-click-through vs. cost-per-action. In System Sciences (HICSS), 2010 43rd Hawaii International Conference on.
18. Kumar, S., Jacob, V., and Sriskandarajah, C. Scheduling advertisements on a web page to maximize revenue. European Journal of Operational Research 173, 3 (2006), 1067-1089.
19. Kwon, C. Single period balancing of pay-per-click and pay-per-view online display advertisements. Journal of Revenue & Pricing Management 10, 3 (2011), 261-270.
20. Li, Y., and Jhang-Li, J. Pricing display ads and contextual ads: Competition, acquisition, and investment. Electronic Commerce Research and Applications 8, 1 (2009), 16-27.
21. Mangani, A. Online advertising: Pay-per-view versus pay-per-click. Journal of Revenue & Pricing Management 2, 4 (2004), 295-302.
22. Mohan, N. The adsense revenue share. http://goo.gl/yP4Ln, 2010.
23. Pandey, S., Chakrabarti, D., and Agarwal, D. Multi-armed bandit problems with dependent arms. In Proceedings of the ICML 07'.
24. Ribeiro-Neto, B., Cristo, M., Golgher, P., and Silva de Moura, E. Impedance coupling in content-targeted advertising. In Proceedings of the ACM-SIGIR 05'.
25. Richardson, M., Dominowska, E., and Ragno, R. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the WWW '07.
26. Roels, G., and Fridgeirsdottir, K. Dynamic revenue management for online display advertising. Journal of Revenue & Pricing Management 8, 5 (2009), 452-466.
27. Spaan, M., and Vlassis, N. Perseus: Randomized point-based value iteration for POMDPs. Journal of artificial intelligence research 24, 1 (2005), 195-220.
28. Tang, J., Liu, N., Yan, J., Shen, Y., Guo, S., Gao, B., Yan, S., and Zhang, M. Learning to rank audience for behavioral targeting in display ads. In Proceedings of the ACM-CIKM 11'.
29. Thrun, S. Monte carlo POMDPs. Advances in neural information processing systems 12 (2000), 1064-1070.
30. Turner, J., Scheller-Wolf, A., and Tayur, S. Scheduling of dynamic in-game advertising. Operations research 59, 1 (2011), 1-16.
31. Tyler, S., Pandey, S., Gabrilovich, E., and Josifovski, V. Retrieval models for audience selection in display advertising. In Proceedings of the ACM-CIKM 11'.
32. Wang, C., Zhang, P., Choi, R., and Eredita, M. Understanding consumers attitude toward advertising. In The 8th Americas conference on information systems.
33. Wu, Z., Xu, G., Pan, R., Zhang, Y., Hu, Z., and Lu, J. Leveraging wikipedia concept and category information to enhance contextual advertising. In Proceedings of the ACM-CIKM 11'.