Strategic bidder behavior in sponsored search auctions
Decision Support Systems
Revenue analysis of a family of ranking rules for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
Improving ad relevance in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
Stochastic variability in sponsored search auctions: observations and models
Proceedings of the 12th ACM conference on Electronic commerce
Ad impression forecasting for sponsored search
Proceedings of the 22nd international conference on World Wide Web
A predictive model for advertiser value-per-click in sponsored search
Proceedings of the 22nd international conference on World Wide Web
Predicting advertiser bidding behaviors in sponsored search by rationality modeling
Proceedings of the 22nd international conference on World Wide Web
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
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There are two kinds of bidders in sponsored search: most keep their bids static for long periods of time, but some do actively manage their bids. In this work we develop a model of bidder behavior in sponsored search that applies to both active and inactive bidders. Our observations on real keyword auction data show that advertisers see substantial variation in rank, even if their bids are static. This motivates a discrete choice approach that bypasses bids and directly models an advertiser's (perhaps passive) choice of rank. Our model's value per click estimates are consistent with basic theory which states that bids should not exceed values, even though bids are not directly used to fit the model. An empirical evaluation confirms that our model performs well in terms of predicting realized ranks and clicks.