Scenario-Wise-Rec, an open-sourced benchmark for Multi-Scenario/Multi-Domain Recommendation. We provide 10 models across 6 different datasets.
Dataset information is listed as follows:
| Dataset | Domain number | Interaction | User | Item |
|---|---|---|---|---|
| MovieLens | Domain 0 | 210,747 | 1,325 | 3,429 |
| Domain 1 | 395,556 | 2,096 | 3,508 | |
| Domain 2 | 393,906 | 2,619 | 3,595 | |
| KuaiRand | Domain 0 | 2,407,352 | 961 | 1,596,491 |
| Domain 1 | 7,760,237 | 991 | 2,741,383 | |
| Domain 2 | 895,385 | 171 | 332,210 | |
| Domain 3 | 402,366 | 832 | 547,908 | |
| Domain 4 | 183,403 | 832 | 43,106 | |
| Ali-CCP | Domain 0 | 32,236,951 | 89,283 | 465,870 |
| Domain 1 | 639,897 | 2,561 | 188,610 | |
| Domain 2 | 52,439,671 | 150,471 | 467,122 | |
| Tenrec | Domain 0 | 64,475,979 | 997,263 | 1,365,660 |
| Domain 1 | 54,277,815 | 989,911 | 791,826 | |
| Domain 2 | 1,588,512 | 455,636 | 152,601 | |
| Douban | To be added | - | - | - |
| Mind | To be added | - | - | - |
Models information is listed as follows:
| Model | model-name | Link |
|---|---|---|
| Shared Bottom | SharedBottom | Link |
| MMOE | MMOE | Link |
| PLE | PLE | Link |
| SAR-Net | sarnet | Link |
| STAR | star | Link |
| M2M | m2m | Link |
| AdaSparse | adasparse | Link |
| AdaptDHM | adaptdhm | Link |
| EPNet | ppnet | Link |
| PPNet | epnet | Link |
We provide a Python package scenario_wise_rec for users. Simply run:
pip install -i https://test.pypi.org/simple/ scenario-wise-recNote that the pip installation could be behind the recent updates. So, if you want to use the latest features or develop based on our code, you should install via source code.
First, clone the repo:
git clone https://github.com/Xiaopengli1/Scenario-Wise-Rec.gitThen,
cd Scenario-Wise-Recthen use pip to install our packages:
pip install .We provide examples for users. See /examples/multi_domain_ranking/, and dataset samples are provided in /examples/multi_domain_ranking/data. You could directly test it by simply do:
python run_ali_ccp_ctr_ranking_multi_domain.py --model starFor Full-Dataset Download and test, refer to the following steps.
Four Multi-Scenario/Multi-Domain Datasets are provided. See the following Table.
| Dataset | Domain Number | Users | Items | Items | Download |
|---|---|---|---|---|---|
| Movie-Lens | 3 | 6k | 4k | 1M | ML_Download |
| KuaiRand | 5 | 1k | 4M | 11M | KR_Download |
| Ali-CCP | 3 | 238k | 467k | 85M | AC_Download |
| Tenrec | 3 | 1M | 2M | 120M | TR_Download |
| Douban | 3 | - | - | - | DB_Download |
| Mind | 4 | - | - | - | MD_Download |
Substitute the full-dataset with sampled dataset, at the same time, uncomment the code in the accordingly python-script.
python run_movielens_rank_multi_domain.py --model_name star --device "cuda:0" --seed 2022 We offer two template files run_example.py and base_example.py for a pipeline to help you to process different multi-scenario dataset and your own multi-scenario models.
see run_example.py.
During the function get_example_dataset(input_path) to process your dataset. Be noted the feature
"domain_indicator" is the feature to indicate domains. For other implementation details refer the file.
see base_example.py. Where you could build your own model here, where we left two spaces for users to implement scenario-shared and scenario-specific models. We also leave comments on how to format the output dimension. Please refer to the file to see more details.
We welcome any contribution that could help improve the benchmark, please fork the repo and create a pull request. You can also open an issue if you have any questions. Don't forget to give the project a star! Thanks again!
Our code is referred to Torch-RecHub. Thanks to their contribution.