MuLe

This is the official code for MuLe (Multi-Grained Graph Learning for Multi-Behavior Recommendation), accepted to CIKM 2024.

Prerequisties

You can install the required packages with a conda environment by typing the following command in your terminal:

conda create -n MULE python=3.9
conda activate MULE
pip install -r requirements.txt

Datasets

The statistics of datasets used in MuLe are summarized as follows. The percentage of each auxiliary behavior is the ratio of target intersected interactions (e.g., view and buy).

Dataset	Users	Items	Views	Collects	Carts	Buys
Taobao	15,449	11,953	873,954 (9%)	-	195,476 (10%)	92,180
Tmall	41,738	11,953	1,813,498 (12%)	221,514 (12%)	1,996 (15%)	255,586
Jdata	93,334	24,624	1,681,430 (16%)	45,613 (43%)	49,891 (57%)	321,883

We gathered Tmall and Jdata datasets from CRGCN and Taobao dataset from MBCGCN. To preprocess the datasets for use in our code, type the following command:

python ./data/preprocess.py

Usage

Train our model from scratch

You can train the model with the best hyperparameters for each dataset by typing the following command in your terminal:

Train MuLE in the Taobao dataset

python ./src/main.py --dataset taobao \
                     --lr 1e-4 \
                     --weight_decay 0 \
                     --tda_layers 4 \
                     --gnn_layers 1 \
                     --emb_dim 64 \
                     --num_epochs 100 \
                     --batch_size 1024

Train MuLE in the Tmall dataset

python ./src/main.py --dataset tmall \
                     --lr 5e-4 \
                     --weight_decay 1e-5 \
                     --tda_layers 5 \
                     --gnn_layers 1 \
                     --emb_dim 64 \
                     --num_epochs 100 \
                     --batch_size 1024

Train MuLE in the Jdata dataset

python ./src/main.py --dataset jdata \
                     --lr 1e-3 \
                     --weight_decay 0 \
                     --tda_layers 5 \
                     --gnn_layers 1 \
                     --emb_dim 64 \
                     --num_epochs 100 \
                     --batch_size 1024

Use a pre-trained MuLe

Because of the volume limit of the github, we provide the pre-trained parameters of MuLe for each dataset in google drive. You can download the pre-trained files as follows:

gdown --folder https://drive.google.com/drive/folders/1L5wnVGQ6EhBy9wyPJTOxsU9JD7zIIvrc 

After downloading the pre-trained parameters, you can run the pre-trained model by adding --load_checkpoint option to the above training command.

Result of Pre-trained MuLe

The test performance of the pre-trained MuLE for each dataset is as follows:

Dataset	HR@10	NDCG@10
Taobao	0.1939	0.1109
Tmall	0.2109	0.1165
Jdata	0.5820	0.4147

All experiments are conducted on RTX 4090 (24GB) with cuda version 11.8, and the above results were reproduced with the random seed seed=42.

The reported results in the paper are as follows:

HR@10	Taobao	Tmall	Jdata
LightGCN	0.0411	0.0393	0.2252
CRGCN	0.0855	0.0840	0.5001
MB-CGCN	0.1233	0.0984	0.4349
HPMR	0.1104	0.0956	-
PKEF	0.1385	0.1277	0.4334
MB-HGCN	0.1299	0.1443	0.5406
MuLE	0.1918	0.2112	0.5889
% diff	38.5%	44.6%	10.3%

NDCG@10	Taobao	Tmall	Jdata
LightGCN	0.0240	0.0209	0.1436
CRGCN	0.0439	0.0442	0.2914
MB-CGCN	0.0677	0.0558	0.2758
HPMR	0.0599	0.0515	-
PKEF	0.0785	0.0721	0.2615
MB-HGCN	0.0690	0.0769	0.3555
MuLE	0.1103	0.1177	0.4061
% diff	40.5%	52.9%	25.4%

Validated hyperparameters of MuLe

We provide the validated hyperparameters of MuLe for each dataset to ensure reproducibility.

Dataset	$\eta$	$\lambda$	$L_{\texttt{tda}}$	$L_{\texttt{light}}$	$d$	$T$	$B$
Taobao	1e-4	0	4	1	64	100	1024
Tmall	5e-4	1e-5	5	1	64	100	1024
Jdata	1e-3	0	5	1	64	100	1024

Description of each hyperparameter

• $\eta$: learning rate of the Adam optimizer (--lr)
• $\lambda$: weight decay for L2-regularization (--weight_decay)
• $L_{\texttt{tda}}$: number of TDA's layers (--tda_layers)
• $L_{\texttt{light}}$: number of LightGCN's layers (--gnn_layers)
• $d$: embedding dimension (--emb_dim)
• $T$: number of epochs (--num_epochs)
• $B$: batch size for target data (--batch_size)

Detailed Options

You can train and evaluate your own dataset with custom hyperparameters as follows:

Option	Description	Default
dataset	dataset name	taobao
data_dir	data directory path	./data
checkpoint_dir	checkpoint directory path	./checkpoint
load_checkpoint	whether to load the configuration used in a pre-trained model	False
batch_size	batch size for target data	1024
lr	learning rate	0.0001
weight_decay	strength $\lambda$ of L2 regularization	0.00001
gnn_layers	number of LightGCN layers	1
tda_layers	number of TDA layers	4
emb_dim	embedding dimension	64
num_epochs	number of epochs	100
seed	random seed; If None, the seed is not fixed	42
device	training device	cuda:0
topk	Top-k items	10

Citation

Please cite the paper if you use this code in your own work:

@inproceedings{10.1145/3627673.3679709,
author = {Lee, Seunghan and Ko, Geonwoo and Song, Hyun-Je and Jung, Jinhong},
title = {MuLe: Multi-Grained Graph Learning for Multi-Behavior Recommendation},
year = {2024},
booktitle = {ACM International Conference on Information and Knowledge Management},
url = {https://doi.org/10.1145/3627673.3679709}
}