Adaptive mechanism design: a metalearning approach
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Strategic bidder behavior in sponsored search auctions
Decision Support Systems
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Revenue analysis of a family of ranking rules for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
A semantic approach to contextual advertising
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing relevance and revenue in ad search: a query substitution approach
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engine revenue in sponsored search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Revenue optimization with relevance constraint in sponsored search
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
Exploitation and exploration in a performance based contextual advertising system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Stochastic variability in sponsored search auctions: observations and models
Proceedings of the 12th ACM conference on Electronic commerce
Discrete choice models of bidder behavior in sponsored search
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Efficient ranking in sponsored search
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Hi-index | 0.00 |
Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature that investigate how to design an auction mechanism in order to optimize the revenue of the search engine. However, due to some unrealistic assumptions used, the practical values of these studies are not very clear. In this paper, we propose a novel game-theoretic machine learning approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimization framework. In particular, we first learn a Markov model from historical data to describe how advertisers change their bids in response to an auction mechanism, and then for any given auction mechanism, we use the learnt model to predict its corresponding future bid sequences. Next we learn the auction mechanism through empirical revenue maximization on the predicted bid sequences. We show that the empirical revenue will converge when the prediction period approaches infinity, and a Genetic Programming algorithm can effectively optimize this empirical revenue. Our experiments indicate that the proposed approach is able to produce a much more effective auction mechanism than several baselines.