How to Improve Representation Alignment and Uniformity in Graph-based Collaborative Filtering?

Welcome to the official code repository for the ICWSM 2024 paper paper "How to Improve Representation Alignment and Uniformity in Graph-based Collaborative Filtering?". The code structure adapts from SELFRec.

Introduction

Our framework performs self-supervised contrastive learning on the user and item representations from the perspective of label-irrelevant alignment and uniformity, in addition to lable-relevant representation alignment and uniformity. With representations less dependent on label supervision, our framework therefore captures more label-irrelevant data structures and patterns, leading to more generalized representation alignment and uniformity.

Getting Started

1. Preparation:

   • Create a directory named results/ in the project's root directory to store output files.

2. Configurations:

   • Navigate to conf/AUPlus.conf to adjust model settings, including hyper-parameters and dataset specifications. The mode parameter can be set as follows:
     • 0: The default AU⁺ model.
     • 1: The AU⁺-AU variant, where augmented views are restrained with the alignment and uniformity losses.
     • 2: The AU⁺-SGL (edge drop) variant, where augmented views are generated via edge drop in SGL.

3. Run the Model: From the root directory, run:

   python main.py --model=AUPlus
   

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{ouyang2024auplus
    title={How to Improve Representation Alignment and Uniformity in Graph-based Collaborative Filtering?},
    author={Zhongyu Ouyang and Chunhui Zhang and Shifu Hou and Chuxu Zhang and Yanfang Ye},
    booktitle={International AAAI Conference on Web and Social Media},
    year={2024}
}