The adwords problem: online keyword matching with budgeted bidders under random permutations
Proceedings of the 10th ACM conference on Electronic commerce
Bidding for Representative Allocations for Display Advertising
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Online Stochastic Matching: Beating 1-1/e
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Optimal online assignment with forecasts
Proceedings of the 11th ACM conference on Electronic commerce
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Real-time bidding algorithms for performance-based display ad allocation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Simultaneous approximations for adversarial and stochastic online budgeted allocation
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Partner tiering in display advertising
Proceedings of the 7th ACM international conference on Web search and data mining
| Hi-index | 0.00 |
Motivated by the problem of optimizing allocation in guaranteed display advertising, we develop an efficient, lightweight method of generating a compact allocation plan that can be used to guide ad server decisions. The plan itself uses just O(1) state per guaranteed contract, is robust to noise, and allows us to serve (provably) nearly optimally. The optimization method we develop is scalable, with a small in-memory footprint, and working in linear time per iteration. It is also "stop-anytime", meaning that time-critical applications can stop early and still get a good serving solution. Thus, it is particularly useful for optimizing the large problems arising in the context of display advertising. We demonstrate the effectiveness of our algorithm using actual Yahoo! data.