Optimal Bidding Strategy with Smooth Budget Delivery in Online Advertising
Bibliographic record
Abstract
In this paper, the optimal bidding strategy with smooth budget delivery in a real-time bidding (RTB) platform is addressed. Feedback control theory plays an essential role in the performance enhancement of ad campaigns in online advertising industry. The objective is to determine the optimal bidding prices as control signals such that (i) the total number of clicks by visiting users is maximized, and (ii) the campaign budget in every episode is smoothly delivered without a premature finishing of the campaign budget or excessive spending rates. In this paper, the advertisers are regarded as the agents in a Markov decision process, where the rewards are chosen according to the main campaign objectives. An advertiser is supposed to select a sequence of bidding actions in terms of a control policy such that a long-term accumulation of the rewards is maximized. It is shown that the smooth bidding with real-time adaptation fits into the framework of reinforcement learning with dynamic programming. Accordingly, an approximation algorithm is proposed to solve the corresponding Bellman optimality equation. The results are utilized to form the bidding strategy with smooth budget delivery. Simulation results on a real-world dataset confirm that the proposed approach outperforms the state-of-the-art bidding strategies, by sustainable participation in the auctions, and maximizing the number of user clicks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".