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 — Full literature survey: 32 papers across bidding algorithms, CTR prediction, and clearing price models
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