Budget-Constrained Real-Time Bidding Optimization
| Autor: | Ming-Syan Chen, Chi-Chun Lin, Kun-Ta Chuang, Wush Chi-Hsuan Wu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
General Computer Science Demand-side platform Computer science 02 engineering and technology Real-time bidding Bidding Impression Advertising campaign Knapsack problem 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Predictive modelling Budget constraint |
| Zdroj: | ACM Transactions on Knowledge Discovery from Data. 14:1-27 |
| ISSN: | 1556-472X 1556-4681 |
| Popis: | In this article, we pursue a better solution for the promising problem, i.e., the bidding strategy design, in the real-time bidding (RTB) advertising (AD) environment. Under the budget constraint, the design of an optimal strategy for bidding on each incoming impression opportunity targets at acquiring as many clicks as possible during an AD campaign. State-of-the-art bidding algorithms rely on a single predictor, the clickthrough rate predictor, to calculate the bidding value for each impression. This provides reasonable performance if the predictor has appropriate accuracy in predicting the probability of user clicking. However, the classical methods usually fail to capture optimal results since the predictor accuracy is limited. We improve the situation by accomplishing an additional winning price predictor in the bidding process. In this article, an algorithm combining powers of multiple prediction models is developed. It emerges from an analogy to the online stochastic knapsack problem, and the efficiency of the algorithm is also theoretically analyzed. Experiments conducted on real world RTB datasets show that the proposed solution performs better with regard to both number of clicks achieved and effective cost per click in many different settings of budget constraints. |
| Databáze: | OpenAIRE |
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