A History of Control Engineering 1930-1955
A History of Control Engineering 1930-1955
Modern Control Technology
A combinatorial allocation mechanism with penalties for banner advertising
Proceedings of the 17th international conference on World Wide Web
Adaptive bidding for display advertising
Proceedings of the 18th international conference on World wide web
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Fast algorithms for finding matchings in lopsided bipartite graphs with applications to display ads
Proceedings of the 11th ACM conference on Electronic commerce
Feedback Systems: An Introduction for Scientists and Engineers
Feedback Systems: An Introduction for Scientists and Engineers
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Truthful auctions with optimal profit
WINE'06 Proceedings of the Second international conference on Internet and Network Economics
Handling forecast errors while bidding for display advertising
Proceedings of the 21st international conference on World Wide Web
Estimating conversion rate in display advertising from past erformance data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
SHALE: an efficient algorithm for allocation of guaranteed display advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Online allocation of display ads with smooth delivery
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Marketing campaign evaluation in targeted display advertising
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
Real-time bid optimization for group-buying ads
Proceedings of the 21st ACM international conference on Information and knowledge management
Optimizing budget constrained spend in search advertising
Proceedings of the sixth ACM international conference on Web search and data mining
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We describe a real-time bidding algorithm for performance-based display ad allocation. A central issue in performance display advertising is matching campaigns to ad impressions, which can be formulated as a constrained optimization problem that maximizes revenue subject to constraints such as budget limits and inventory availability. The current practice is to solve the optimization problem offline at a tractable level of impression granularity (e.g., the page level), and to serve ads online based on the precomputed static delivery scheme. Although this offline approach takes a global view to achieve optimality, it fails to scale to ad allocation at the individual impression level. Therefore, we propose a real-time bidding algorithm that enables fine-grained impression valuation (e.g., targeting users with real-time conversion data), and adjusts value-based bids according to real-time constraint snapshots (e.g., budget consumption levels). Theoretically, we show that under a linear programming (LP) primal-dual formulation, the simple real-time bidding algorithm is indeed an online solver to the original primal problem by taking the optimal solution to the dual problem as input. In other words, the online algorithm guarantees the offline optimality given the same level of knowledge an offline optimization would have. Empirically, we develop and experiment with two real-time bid adjustment approaches to adapting to the non-stationary nature of the marketplace: one adjusts bids against real-time constraint satisfaction levels using control-theoretic methods, and the other adjusts bids also based on the statistically modeled historical bidding landscape. Finally, we show experimental results with real-world ad delivery data that support our theoretical conclusions.