https://github.com/rmanluo/mamdr
Official code implementation for ICDE 23 paper MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
https://github.com/rmanluo/mamdr
deep-learning meta-learning multi-domain-learning recommender-system
Last synced: about 1 year ago
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Official code implementation for ICDE 23 paper MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
- Host: GitHub
- URL: https://github.com/rmanluo/mamdr
- Owner: RManLuo
- License: mit
- Created: 2022-02-05T03:14:52.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-11-27T03:38:56.000Z (over 2 years ago)
- Last Synced: 2024-05-22T00:06:30.527Z (about 2 years ago)
- Topics: deep-learning, meta-learning, multi-domain-learning, recommender-system
- Language: Python
- Homepage:
- Size: 1.78 MB
- Stars: 32
- Watchers: 3
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
Official code implementation for ICDE 23 paper MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation.
[\[Paper\]](https://arxiv.org/abs/2202.12524)
[\[Slides\]](./ICDE-23-Slides-MAMDR.pdf)
## Requirements
```text
tensorflow-gpu==1.12.0
requests==2.26.0
tqdm==4.62.2
pandas==1.1.5
scikit-learn==0.24.2
numpy==1.16.6
deepctr==0.9.0
```
* RTX 2080 + 64G RAM
* python: 3.6
* Ubuntu 20.04
## Data Preprocess
### Amazon dataset
Enter `dataset/Amazon`
raw dataset at：https://nijianmo.github.io/amazon/index.html#complete-data
1. change the split rule in config_*.json.
2. run `split.py --config config_*.json`. It will automatically download and split the domains.
### Taobao dataset
Enter `dataset/Taobao`
1. [download dataset](https://tianchi.aliyun.com/dataset/dataDetail?dataId=9716)
2. unzip the dataset into `raw_data`
1. theme_click_log.csv
2. theme_item_pool.csv
3. user_item_purchase_log.csv
4. item_embedding.csv
5. user_embedding.csv
3. change the dataset config in `config_*.json`. theme_num = -1 denotes using all domains.
4. run `split.py --config config_*.json` to create dataset.
## Preprocessed dataset
You can download the datasets and extract the folders into `dataset/Amazon` and `dataset/Taobao`, respectively.
* [Amazon-6/13](https://drive.google.com/file/d/1JpzaDZioLTlGMJdiLIvrP8m9ngOGe7dI/view?usp=sharing)
* [Taobao-10/20/30](https://drive.google.com/file/d/1L-Y6KZ5-DWKYLspaLo0oxPAQ-MxUgIKe/view?usp=sharing)
## Run experiments
### Run baselines
* change model name in `config.json`
``` python
python3 run.py --config config/Taobao-10/deepctr.json
```
### Run Domain Negotiation
``` python
python3 run.py --config config/Taobao-10/deepctr_DN.json
```
### Run MAMDR
``` python
python3 run.py --config config/Taobao-10/deepctr_DN+DR.json
```
## Config Description
Model Name：
``basemodel_extenstion(s)``
### Base model：
single domain： `mlp`, `wdl`, `nfm`, `autoint`, `deepfm`
multi tasks： `shared_bottom`, `mmoe`, `ple`
multi domain： `star`
### Extenstion：
```json
default: joint learning
separate: separatly train for each domain
finetune: finetune after joint training
meta: training using MAML
reptile: reptile meta learning
mldg: MLDG
uncertainty_weight: weighted loss
pcgrad: pcgrad
domain_negotiation: domain negotiation meta learning
mamdr: DN + DR
batch: using batch for meta learning
```
#### Example
* MLP + Joint:`mlp`
* DeepFM + Joint: `deepfm`
* MLP + Joint + Finetune:`mlp_finetune`
* MLP + MAML:`mlp_meta_finetune`
* MLP + DN: `mlp_meta_domain_negotiation_finetune_`
* MLP + DN + DR : `mlp_meta_mamdr_finetune`
You can find more examples in `config/`.
### Config example
```json
{
"model": {
"name": "mlp_meta_mamdr_finetune", // model name
"norm": "none", // noarmalization method for star: none, bn, pn
"dense": "dense", // dense for star: dense, star
"auxiliary_net": false,
"user_dim": 128,
"item_dim": 128,
"domain_dim": 128,
"auxiliary_dim": 128,
"hidden_dim": [ // hidden sizes for hidden layers
256,
128,
64
],
"dropout": 0.5
},
"train": {
"load_pretrain_emb": true, // whether load pretrain embding, only support for Taobao
"emb_trainable": false, // whether train embedding
"epoch": 99999,
"learning_rate": 0.001, // inner learning rate
"meta_learning_rate": 0.1, // outer learning rate
"domain_meta_learning_rate": 0.1, // Not used
"merged_method": "plus",
"sample_num": 5, // sample number for DR
"add_query_domain": true,
"finetune_every_epoch": false,
"shuffle_sequence": true, // whether shuffle the domain sequence
"meta_sequence": "random",
"target_domain": -1, // disabled
"domain_regulation_step": 0, // disabled
"meta_train_step": 0, // disabled
"meta_finetune_step": 0, // disabled
"meta_split": "train-train", // how to split the query and support set for meta-learning: train-train, meta-train/val, meta-train/val-no-exclusive
"meta_split_ratio": 0.8,
"average_meta_grad": "none",
"meta_parms": [
"all"
],
"result_save_path": "result",
"checkpoint_path": "checkpoint",
"loss": "binary_crossentropy",
"optimizer": "adam",
"patience": 3,
"val_every_step": 1,
"histogram_freq": 0,
"shuffle_buff_size": 10000
},
"dataset": {
"name": "Taobao",
"dataset_path": "dataset/Taobao",
"domain_split_path": "split_by_theme_10",
"batch_size": 1024,
"shuffle_buffer_size": 10000,
"num_parallel_reads": 8,
"seed": 123
}
}
```