Dynamic Bidding Strategy Based on Probabilistic Feedback in Display Advertising

Wang, Yuzhu; Pan, Shumin; Zhang, Qianwen; Xie, Jinkui

doi:10.1007/978-3-319-70096-0_86

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Among other subfields of online AD, RTB has attracted more and more research interests in recent years. Works in RTB concern various topics including bid landscape forecasting [3,40,42], bid WP prediction [32], and bidding strategy design (BSD) [7,21,24,25,33,37,38]. Interested readers can refer to [30,35] for the comprehensive review.…”

Section: Related Workmentioning

confidence: 99%

Budget-Constrained Real-Time Bidding Optimization

Lin

Chuang

et al. 2020

ACM Trans. Knowl. Discov. Data

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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.

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