Truthful auctions for pricing search keywords
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
An analysis of alternative slot auction designs for sponsored search
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Revenue analysis of a family of ranking rules for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
Graphical Models, Exponential Families, and Variational Inference
Graphical Models, Exponential Families, and Variational Inference
Ranking and tradeoffs in sponsored search auctions
Proceedings of the fourteenth ACM conference on Electronic commerce
Revenue optimization in the generalized second-price auction
Proceedings of the fourteenth ACM conference on Electronic commerce
Query clustering based on bid landscape for sponsored search auction optimization
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A game- heoretic machine learning approach for revenue maximization in sponsored search
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Counterfactual reasoning and learning systems: the example of computational advertising
The Journal of Machine Learning Research
Machine learning in an auction environment
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
In the standard model of sponsored search auctions, an ad is ranked according to the product of its bid and its estimated click-through rate (known as the quality score), where the estimates are taken as exact. This paper re-examines the form of the efficient ranking rule when uncertainty in click-through rates is taken into account. We provide a sufficient condition under which applying an exponent--strictly less than one--to the quality score improves expected efficiency. The condition holds for a large class of distributions known as natural exponential families, and for the lognormal distribution. An empirical analysis of Yahoo's sponsored search logs reveals that exponent settings substantially smaller than one can be efficient for both high and low volume keywords, implying substantial deviations from the traditional ranking rule.