Name
Abstract | ||
|---|---|---|
Real-Time Bidding allows an advertiser to purchase media inventory through an auction system that unfolds in the order of milliseconds. Media providers are increasingly being integrated into such programmatic buying platforms. It is typical for a contemporary Real-Time Bidding system to receive millions of bid requests per second at peak time, and have a large portion of these to be irrelevant to any advertiser. Meanwhile, given a valuable bid request, tens of thousands of advertisements might be qualified for scoring. We present our efforts in building selection models for both bid requests and advertisements to handle this scalability challenge. Our bid request model treats the system load as a hierarchical resource allocation problem and directs traffic based on the estimated quality of bid requests. Next, our exploration/exploitation advertisement model selects a limited number of qualified advertisements for thorough scoring based on the expected value of a bid request to the advertiser given its features. Our combined bid request and advertisement model is able to win more auctions and bring more value to clients by stabilizing the bidding pipeline. We empirically show that our deployed system is capable of handling 5x more bid requests. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDM.2015.72 | IEEE International Conference on DataMining |
Field | DocType | ISSN |
Auction sniping,Data mining,Computer science,Server,Online advertising,Resource allocation,Real-time bidding,Common value auction,Bidding,Scalability | Conference | 1550-4786 |
Citations | PageRank | References |
3 | 0.38 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqiang Shen | 1 | 236 | 17.86 |
| Burkay Orten | 2 | 98 | 6.00 |
| Sahin Cem Geyik | 3 | 115 | 11.71 |
| Daniel Liu | 4 | 3 | 0.38 |
| Shahriar Shariat | 5 | 31 | 4.34 |
| Fang Bian | 6 | 3 | 0.38 |
| Ali Dasdan | 7 | 849 | 73.11 |