HCMKR

Hyperbolic Contrastive Learning with Model-Augmentation for Knowledge-Aware Recommendation (HCMKR)

This is the Pytorch implementation for our ECML-PKDD'24 paper: Hyperbolic Contrastive Learning with Model-Augmentation for Knowledge-Aware Recommendation.

Abstract

<div style="text-align: justify;"> Benefiting from the effectiveness of graph neural networks (GNNs) and contrastive learning, GNN-based contrastive learning has become mainstream for knowledge-aware recommendation. However, most existing contrastive learning-based methods have difficulties in effectively capturing the underlying hierarchical structure within user-item bipartite graphs and knowledge graphs. Moreover, they commonly generate positive samples for contrastive learning by perturbing the graph structure, which may lead to a shift in user preference learning. To overcome these limitations, we propose hyperbolic contrastive learning with model-augmentation for knowledge-aware recommendation. To capture the intrinsic hierarchical graph structures, we first design a novel Lorentzian knowledge aggregation mechanism, which enables more effective representations of users and items. Then, we propose three model-level augmentation techniques to assist Hyperbolic contrastive learning. Different from the classical structure-level augmentation (e.g., edge dropping), the proposed model-augmentations can avoid preference shifts between the augmented positive pair. The overall framework is as follows: <div> <br>

Environment Requirement

# More details can be seen in ./code/packages.txt.
torch==1.8.1+cu111 
torch-cluster==1.5.9  
torch-scatter==2.0.6  
torch-sparse==0.6.11  
torch-spline-conv==1.2.1  
torch-geometric==1.7.2

Dataset

We provide three processed datasets (yelp2018, amazon-book, and ml-20m). You can download them from link, and put them in the file ./code.

Run the Code

cd code && bash performance.sh

Acknowledgment of Open-Source Code Contributions

The code is based on the open-source repositories: LightGCN and KGCL, many thanks to the authors!

You are welcome to cite our paper:

@inproceedings{SunMa25,
  author = {Sun, Shengyin and Chen, Ma},
  title = {Hyperbolic Contrastive Learning with Model-Augmentation for Knowledge-Aware Recommendation},
  year = {2024},
  booktitle = {Machine Learning and Knowledge Discovery in Databases},
  pages = {199–217}
}