Bandit Algorithms Applied in Online Advertisement to Evaluate Click-Through Rates
Bibliographic record
Abstract
Reinforcement learning approaches are increasingly used to model complex decision-based problems. The multi-armed bandit problem is a classical instance suitable for reinforcement learning challenges that involves balancing exploration and exploitation trade-offs. Finding a balance between exploration and exploitation is a fundamental aspect of a variety of reinforcement learning applications. Multi-armed bandit algorithms are useful in multiple industry domains such as computer games, clinical trials, telecommunication, and recommender systems. This paper aims to study the multi-armed bandit problem and contextualize the algorithms to provide a framework for optimizing click-through rates in online advertising, thereby improving the customer fidelity. To that end, parameterized bandit algorithms such as upper confidence bound (UCB), epsilon greedy (є-greedy), and SoftMax algorithms were implemented and tweaked to maximize performance in an advertising platform. The results obtained demonstrate optimal records in choosing the best adverts. The UCB approach achieves the highest cumulative mean rewards for selecting the arms over the iterations. Experiments stipulate that the proposed system outperforms the conventional techniques when є and τ are set to 0.1 as it does not rely on the availability of the data over varying cycles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".