1. Introduction

Scenario-Wise Rec, an open-sourced benchmark for Multi-Scenario/Multi-Domain Recommendation.

Dataset introduction

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
Amazon	Domain 0	198,502	22,363	12,101
	Domain 1	278,677	39,387	23,033
	Domain 2	346,355	38,609	18,534
Douban	Domain 0	227,251	2,212	95,872
	Domain 1	179,847	1,820	79,878
	Domain 2	1,278,401	2,712	34,893
Mind	Domain 0	26,057,579	737,687	8,086
	Domain 1	11,206,494	678,268	1,797
	Domain 2	10,237,589	696,918	8,284
	Domain 3	9,226,382	656,970	1,804

Model introduction

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

2. Installation

WARNING: Our package is still being modified and developed, so if there are usage problems, feel free to post issues.

Install via GitHub (Recommended)

First, clone the repo:

git clone https://github.com/Xiaopengli1/Scenario-Wise-Rec.git

Then,

cd Scenario-Wise-Rec

then use pip to install our packages:

pip install .

3. Usage

We provide running scripts for users. See /scripts, and dataset samples are provided in /scripts/data. You could directly test it by simply do:

python run_ali_ccp_ctr_ranking_multi_domain.py --model [model_name]

For Full-Dataset download and test, refer to the following steps.

Step 1: Full Datasets Download

Four Multi-Scenario/Multi-Domain Datasets are provided. See the following table.

Dataset	Domain Number	Users	Items	Interaction	Download
Movie-Lens	3	6k	4k	1M	ML_Download
KuaiRand	5	1k	4M	11M	KR_Download
Ali-CCP	3	238k	467k	85M	AC_Download
Amazon	3	85k	54k	823k	TR_Download
Douban	3	2k	210k	1.7M	DB_Download
Mind	4	748k	20k	56M	MD_Download

Substitute the full-dataset with sampled dataset.

Step 2: Run the code

python run_movielens_rank_multi_domain.py --dataset_path [path] --model_name [model_name] --device ["cpu"/"cuda:0"] --epoch [maximum epoch] --learning_rate [1e-3/1e-5] --batch_size [2048/4096] --seed [random seed] 

4. Build Your Own Multi-scenario Dataset/Model

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.

Instructions on processing your dataset

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.

Instructions on building your model

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.

5. Contributing

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!

6. Credits

Our framework is referred to Torch-RecHub. Thanks to their contribution.