On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Greedy bidding strategies for keyword auctions
Proceedings of the 8th ACM conference on Electronic commerce
Vindictive bidding in keyword auctions
Proceedings of the ninth international conference on Electronic commerce
Advertising keyword suggestion based on concept hierarchy
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Simulation-based game theoretic analysis of keyword auctions with low-dimensional bidding strategies
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Bid generation for advanced match in sponsored search
Proceedings of the fourth 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
Bid landscape forecasting in online ad exchange marketplace
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding Sponsored Search: Core Elements of Keyword Advertising
Understanding Sponsored Search: Core Elements of Keyword Advertising
Discrete choice models of bidder behavior in sponsored search
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
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We study how an advertiser changes his/her bid prices in sponsored search, by modeling his/her rationality. Predicting the bid changes of advertisers with respect to their campaign performances is a key capability of search engines, since it can be used to improve the offline evaluation of new advertising technologies and the forecast of future revenue of the search engine. Previous work on advertiser behavior modeling heavily relies on the assumption of perfect advertiser rationality; however, in most cases, this assumption does not hold in practice. Advertisers may be unwilling, incapable, and/or constrained to achieve their best response. In this paper, we explicitly model these limitations in the rationality of advertisers, and build a probabilistic advertiser behavior model from the perspective of a search engine. We then use the expected payoff to define the objective function for an advertiser to optimize given his/her limited rationality. By solving the optimization problem with Monte Carlo, we get a prediction of mixed bid strategy for each advertiser in the next period of time. We examine the effectiveness of our model both directly using real historical bids and indirectly using revenue prediction and click number prediction. Our experimental results based on the sponsored search logs from a commercial search engine show that the proposed model can provide a more accurate prediction of advertiser bid behaviors than several baseline methods.