Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation

This is the official code for [HGIB (Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation)].

📝 RecSys 2025

🔬 Overview

In this project, we propose a novel model-agnostic Hierarchical Graph Information Bottleneck (HGIB) framework for multi-behavior recommendation to effectively address these challenges. Following information bottleneck principles, our framework optimizes the learning of compact yet sufficient representations that preserve essential information for target behavior prediction while eliminating task-irrelevant redundancies. To further mitigate interaction noise, we introduce a Graph Refinement Encoder (GRE) that dynamically prunes redundant edges through learnable edge dropout mechanisms. We conduct comprehensive experiments on three real-world public datasets, which demonstrate the superior effectiveness of our framework.

🌟 Environment Setup

Prerequisites

The main prerequisites are listed below:

Python 3.9
torch
tensorboard

And the entire dependencies can be set up by run:

pip install -r requirements.txt

Datasets

We provide the Taobao, Tmall and Jdata datasets in './data' folder.

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

Tmall and Jdata datasets are gathered from CRGCN and Taobao dataset is gathered from MBCGCN.

You can run the following script for preprocessing:

python ./data/preprocess.py

🚀 Getting Started

Train HGIB on the Taobao dataset

python ./src/main.py --dataset taobao --lr 5e-4 

Train HGIB on the Tmall dataset

python ./src/main.py --dataset tmall  --lr 5e-4 

Train HGIB on the Jdata dataset

python ./src/main.py --dataset jdata  --lr 5e-4 --alpha 0.5

❤️ Acknowledgement

Our code is developed based on MuLe.

✅ Cite our work

@inproceedings{10.1145/3705328.3748073,
author = {Zhang, Hengyu and Shen, Chunxu and Sun, Xiangguo and Tan, Jie and Tan, Yanchao and Rong, Yu and Cheng, Hong and Yi, Lingling},
title = {Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation},
year = {2025},
isbn = {9798400713644},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3705328.3748073},
doi = {10.1145/3705328.3748073},
booktitle = {Proceedings of the Nineteenth ACM Conference on Recommender Systems},
pages = {155–164},
numpages = {10},
location = {Prague, Czech Republic},
series = {RecSys '25}
}