| # Bidding Algorithms Benchmark |
|
|
| > Complete comparison framework for real-time bidding (RTB) algorithms in online advertising. |
| > Optimizing for clicks under budget constraints using Lagrangian dual methods. |
|
|
| ## Research Resources |
|
|
| - **[RESEARCH_RESOURCES.md](RESEARCH_RESOURCES.md)** — Full literature survey: 32 papers across bidding algorithms, CTR prediction, and clearing price models |
| - **[AUDIT_TRAIL.md](AUDIT_TRAIL.md)** — Every paper, dataset, codebase, and external resource consulted (44 items) |
|
|
| ## Problem Setup |
|
|
| - **Objective**: Maximize number of clicks |
| - **Constraints**: Total spend ≤ Budget, with k% minimum spend guarantee |
| - **Auction Types**: First-price and second-price |
| - **Core Approach**: Lagrangian dual multiplier with online error gradient descent |
|
|
| ## Algorithms |
|
|
| | Algorithm | Type | Auction | Paper | |
| |-----------|------|---------|-------| |
| | **DualOGD** | Adaptive | First-price | Wang et al. 2023 [2304.13477] | |
| | **DualMirrorDescent** | Adaptive | Second-price | Balseiro et al. 2020 [2011.10124] | |
| | **DualRoS** | Adaptive | Second-price | Feng et al. 2022 [2208.13713] | |
| | **TwoSidedDual** | Adaptive | First-price | Extension (cap + floor) | |
| | **RLB** | RL+MDP | Both | Cai et al. 2017 [1701.02490] | |
| | **Linear** | Static | Both | Baseline | |
| | **ORTB** | Static | Second-price | Zhang et al. 2014 (KDD) | |
|
|
| ## Models |
|
|
| | Model | Task | Architecture | Dataset | |
| |-------|------|-------------|---------| |
| | **FinalMLP** | CTR Prediction | Two-stream MLP + Feature Gating | Criteo_x4 | |
| | **DeepFM** | CTR Prediction | FM + DNN (baseline) | Criteo_x4 | |
| | **TorchSurv** | Clearing Price | Deep Survival (Cox PH) | Simulated | |
| | **EmpiricalCDF** | Win Probability | Non-parametric | Online | |
|
|
| ## Structure |
|
|
| ``` |
| bidding_algorithms_benchmark/ |
| ├── README.md |
| ├── RESEARCH_RESOURCES.md |
| ├── AUDIT_TRAIL.md |
| ├── src/ |
| │ ├── ctr/ |
| │ │ ├── train_finalmlp.py |
| │ │ └── train_deepfm.py |
| │ ├── price/ |
| │ │ ├── empirical_cdf.py |
| │ │ └── torchsurv_model.py |
| │ ├── algorithms/ |
| │ │ ├── dual_ogd.py |
| │ │ ├── dual_mirror_descent.py |
| │ │ ├── dual_ros.py |
| │ │ ├── two_sided_dual.py |
| │ │ ├── rlb.py |
| │ │ └── baselines.py |
| │ └── benchmark/ |
| │ ├── auction_simulator.py |
| │ ├── run_comparison.py |
| │ └── sweep.py |
| ├── configs/ |
| │ ├── finalmlp_criteo.yaml |
| │ └── sweep_config.yaml |
| ├── results/ |
| └── requirements.txt |
| ``` |
|
|
