Bid landscape forecasting in online ad exchange marketplace

Cui, Ying; Zhang, Ruofei; Wei, Li; Mao, Jianchang

doi:10.1145/2020408.2020454

Cited by 87 publications

(97 citation statements)

References 13 publications

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“…In [7,10], the authors directly model the market price distribution. However, since the winning probability also relies on the CTR estimation, we introduce the correlation between the winning price and the CTR in the estimation of market price distribution.…”

Section: Market Price Distributionmentioning

confidence: 99%

Improving Real-Time Bidding Using a Constrained Markov Decision Process

Sassioui

Varisteas

et al. 2017

Lecture Notes in Computer Science

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Abstract. Online advertising is increasingly switching to real-time bidding on advertisement inventory, in which the ad slots are sold through real-time auctions upon users visiting websites or using mobile apps. To compete with unknown bidders in such a highly stochastic environment, each bidder is required to estimate the value of each impression and to set a competitive bid price. Previous bidding algorithms have done so without considering the constraint of budget limits, which we address in this paper. We model the bidding process as a Constrained Markov Decision Process based reinforcement learning framework. Our model uses the predicted click-through-rate as the state, bid price as the action, and ad clicks as the reward. We propose a bidding function, which outperforms the state-of-the-art bidding functions in terms of the number of clicks when the budget limit is low. We further simulate different bidding functions competing in the same environment and report the performances of the bidding strategies when required to adapt to a dynamic environment.

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Section: Market Price Distributionmentioning

confidence: 99%

Improving Real-Time Bidding Using a Constrained Markov Decision Process

Sassioui

Varisteas

et al. 2017

Lecture Notes in Computer Science

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“…Bid landscape forecasting refers to predicting the distribution of market price for a type of ad inventory [6]. The advertisers use it to calculate the winning rate given a bid and decide the final bid price.…”

Section: Related Workmentioning

confidence: 99%

“…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]. The authors in [12] conducted an error handling methodology to improve the efficiency and reliability of the bid landscape forecasting system.…”

Section: Related Workmentioning

confidence: 99%

“…Various bid landscape models can be utilized to model pz(z), such as the parametric log-normal distribution [6] and Gamma distribution [4]. In this paper, while our model is flexible with various landscape models, we adopt a nonparametric pz(z) directly obtained from each campaign's winning price data [2].…”

Section: Gradient For Risk-returnmentioning

confidence: 99%

“…Solving the proposed learning objectives (6) and (9) relies on the realization of the bidding function b(f θ (x)), the market price distribution pz(z) and the CTR estimation function itself f θ (x). In this section, we will discuss the solutions from the proposed two training objectives given some specific implementations of b(f θ (x)), pz(z) and f θ (x).…”

Section: Model Realizationmentioning

confidence: 99%

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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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