TY - GEN
T1 - Frequency capping in online advertising (extended abstract)
AU - Buchbinder, Niv
AU - Feldman, Moran
AU - Ghosh, Arpita
AU - Naor, Joseph
N1 - Funding Information: Moran Feldman is a recipient of the Google Europe Fellowship in Market Algorithms, and this research is supported in part by this Google Fellowship. This work was supported in part by ISF grant 1366/07 and the Google Interuniversity center for Electronic Markets and Auctions. Funding Information: ★ Moran Feldman is a recipient of the Google Europe Fellowship in Market Algorithms, and this research is supported in part by this Google Fellowship. ★★This work was supported in part by ISF grant 1366/07 and the Google Inter-university center for Electronic Markets and Auctions. 1 In contrast, sponsored search advertisers typically pay per click or per action, and usually have budgets, rather than demands, or quotas, on the number of impressions.
PY - 2011
Y1 - 2011
N2 - We study the following online problem. Each advertiser ai has a value vi, demand di, and frequency cap fi. Supply units arrive online, each one associated with a user. Each advertiser can be assigned at most di units in all, and at most fi units from the same user. The goal is to design an online allocation algorithm maximizing total value. We first show a deterministic upper bound of 3/4-competitiveness, even when all frequency caps are 1, and all advertisers share identical values and demands. A competitive ratio approaching 1 - 1/e can be achieved via a reduction to a model with arbitrary decreasing valuations [GM07]. Our main contribution is analyzing two 3/4-competitive greedy algorithms for the cases of equal values, and arbitrary valuations with equal demands. Finally, we give a primal-dual algorithm which may serve as a good starting point for improving upon the 1 - 1/e ratio.
AB - We study the following online problem. Each advertiser ai has a value vi, demand di, and frequency cap fi. Supply units arrive online, each one associated with a user. Each advertiser can be assigned at most di units in all, and at most fi units from the same user. The goal is to design an online allocation algorithm maximizing total value. We first show a deterministic upper bound of 3/4-competitiveness, even when all frequency caps are 1, and all advertisers share identical values and demands. A competitive ratio approaching 1 - 1/e can be achieved via a reduction to a model with arbitrary decreasing valuations [GM07]. Our main contribution is analyzing two 3/4-competitive greedy algorithms for the cases of equal values, and arbitrary valuations with equal demands. Finally, we give a primal-dual algorithm which may serve as a good starting point for improving upon the 1 - 1/e ratio.
UR - http://www.scopus.com/inward/record.url?scp=80052110004&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-22300-6_13
DO - 10.1007/978-3-642-22300-6_13
M3 - منشور من مؤتمر
SN - 9783642222993
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 158
BT - Algorithms and Data Structures - 12th International Symposium, WADS 2011, Proceedings
T2 - 12th International Symposium on Algorithms and Data Structures, WADS 2011
Y2 - 15 August 2011 through 17 August 2011
ER -