| 1. |
- Du, M., et al.
(författare)
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Improving real-time bidding using a constrained markov decision process
- 2017
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Ingår i: 13th International Conference on Advanced Data Mining and Applications, ADMA 2017. - Cham : Springer. - 9783319691787 ; , s. 711-726
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Konferensbidrag (refereegranskat)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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| 2. |
- Du, M., et al.
(författare)
-
Time series modeling of market price in real-time bidding
- 2019
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Ingår i: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. - : ESANN. ; , s. 643-648
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Konferensbidrag (refereegranskat)abstract
- Real-Time-Bidding (RTB) is one of the most popular online advertisement selling mechanisms. Modeling the highly dynamic bidding environment is crucial for making good bids. Market prices of auctions fluctuate heavily within short time spans. State-of-the-art methods neglect the temporal dependencies of bidders’ behaviors. In this paper, the bid requests are aggregated by time and the mean market price per aggregated segment is modeled as a time series. We show that the Long Short Term Memory (LSTM) neural network outperforms the state-of-the-art univariate time series models by capturing the nonlinear temporal dependencies in the market price. We further improve the predicting performance by adding a summary of exogenous features from bid requests.
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