Programmatic Buying Bidding Strategies with Win Rate and Winning Price Estimation in Real Time Mobile Advertising

Li, Xiang; Guan, Devin

doi:10.1007/978-3-319-06608-0_37

Cited by 24 publications

(12 citation statements)

References 9 publications

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“…In addition, a similar idea of incorporating WP prediction into the bidding value calculation is also considered in [15]. However, only the WP predictor is involved in their work, which is different from our goal to take CTR and WP predictors into account simultaneously.…”

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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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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“…The advertisers use it to calculate the winning rate given a bid and help decide the final bid price. Several winning function forms were hypothesized in [20], [48] to directly induce the optimal bidding functions. A campaignlevel forecasting system with tree models was presented in [9].…”

Section: Related Workmentioning

confidence: 99%

Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

Ren

Zhang

Chang

et al. 2018

IEEE Trans. Knowl. Data Eng.

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Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.

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“…bid landscape, is not studied in above work, and the final utility of the campaign is not considered in the optimization objective, which may result in some unfavorable statistics such as relatively high eCPC and low return-on-investment ratio (ROI). The authors in [15] combined the winning rate estimation and the winning price prediction together and deployed the estimation results in different bidding strategies towards different business demands. The authors in [13] embedded a budget smoothing component into a bid optimization framework.…”

Section: Related Workmentioning

confidence: 99%

“…The advertisers use it to calculate the winning rate given a bid and decide the final bid price. Several winning function forms were hypothesized in [15,32] to directly induce the optimal bidding functions. A campaign-level forecasting system with tree models was presented in [6].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

Ren

Zhang

Rong

et al. 2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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Learning and predicting user responses, such as clicks and conversions, are crucial for many Internet-based businesses including web search, e-commerce, and online advertising. Typically, a user response model is established by optimizing the prediction accuracy, e.g., minimizing the error between the prediction and the ground truth user response. However, in many practical cases, predicting user responses is only part of a rather larger predictive or optimization task, where on one hand, the accuracy of a user response prediction determines the final (expected) utility to be optimized, but on the other hand, its learning may also be influenced from the follow-up stochastic process. It is, thus, of great interest to optimize the entire process as a whole rather than treat them independently or sequentially. In this paper, we take real-time display advertising as an example, where the predicted user's ad click-through rate (CTR) is employed to calculate a bid for an ad impression in the second price auction. We reformulate a common logistic regression CTR model by putting it back into its subsequent bidding context: rather than minimizing the prediction error, the model parameters are learned directly by optimizing campaign profit. The gradient update resulted from our formulations naturally fine-tunes the cases where the market competition is high, leading to a more costeffective bidding. Our experiments demonstrate that, while maintaining comparable CTR prediction accuracy, our proposed user response learning leads to campaign profit gains as much as 78.2% for offline test and 25.5% for online A/B test over strong baselines.

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Programmatic Buying Bidding Strategies with Win Rate and Winning Price Estimation in Real Time Mobile Advertising

Cited by 24 publications

References 9 publications

Budget-Constrained Real-Time Bidding Optimization

Budget-Constrained Real-Time Bidding Optimization

Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

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