This is a repository of the experiment code supporting the paper Real-time Bidding Strategy in Display Advertising: An Empirical Analysis.
Chinese README: README-zh
We use benchmark dataset iPinYou. To download and formalize the iPinYou RTB data, please refers to make-ipinyou-data.
After the formalization, copy train.log.txt and text.log.txt of each campaign (e.g. 1458) to RLBid_EA/data/ipinyou/1458 for further use.
Before training the bidding strategy, you first need to predict the click-through rate of each ad impression. We provide pre-trained FM model parameters for 4 datasets in RLBid_EA/ctr/models. You can use them in conjunction with RLBid_EA/ctr/generate_pctr.py.
You are of course able to train a CTR predictor. RLBid_EA/ctr/model.py implemented 9 classical click-through prediction models.
| Model | Paper | Link |
|---|---|---|
| LR | Predicting clicks: estimating the click-through rate for new ads | [paper] |
| FM | Factorization machines | [paper] |
| FFM | Field-aware factorization machines for CTR prediction | [paper] |
| W&D | Wide & deep learning for recommender systems | [paper] |
| PNN | Product-based neural networks for user response prediction | [paper] |
| DeepFM | DeepFM: a factorization-machine based neural network for CTR prediction | [paper] |
| FNN | Deep learning over multi-field categorical data | [paper] |
| DCN | Deep & cross network for ad click predictions | [paper] |
| AFM | Attentional factorization machines: Learning the weight of feature interactions via attention networks | [paper] |
For details please refer to README .
This repository implements 2 static bidding strategies and 3 dynamic bidding strategies based on reinforcement learning.
| Model | Paper | Link |
|---|---|---|
| LIN | Bid optimizing and inventory scoring in targeted online advertising | [paper] |
| ORTB | Optimal real-time bidding for display advertising | [paper] [code] |
| RLB | Real-time bidding by reinforcement learning in display advertising | [paper] [code] |
| DRLB | Budget constrained bidding by model-free reinforcement learning in display advertising | [paper] |
| FAB | A dynamic bidding strategy based on model-free reinforcement learning in display advertising | [paper] [code] |
For details please refer to the README in each folder.
This project is licensed under the Apache License 2.0.
- @JiaXingBinggan contributed the CTR prediction model code for this repository.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent