Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System

This is our Pytorch implementation for the paper:

Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao (2022). Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System, Paper in arXiv. In SIGIR'22.

Introduction

Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System (MCCLK) is a knowledge-aware recommendation solution based on GNN and Contrastive Learning, proposing a multi-level cross-view contrastive framework to enhance representation learning from multi-faced aspects.

Requirement

The code has been tested running under Python 3.7.9. The required packages are as follows:

• pytorch == 1.5.0
• numpy == 1.15.4
• scipy == 1.1.0
• sklearn == 0.20.0
• torch_scatter == 2.0.5
• torch_sparse == 0.6.10
• networkx == 2.5

Usage

The hyper-parameter search range and optimal settings have been clearly stated in the codes (see the parser function in utils/parser.py).

• Train and Test

python main.py 

Citation

If you want to use our codes and datasets in your research, please cite:

@inproceedings{mcclk2022,
  author    = {Zou, Ding and
               Mao, Xian-Ling and
	       Wang, Ziyang and
	       Qiu, Minghui and
	       Zhu, Feida and
	       Cao, Xin},
  title     = {Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System},
  booktitle = {Proceedings of the 45th International {ACM} {SIGIR} Conference on
               Research and Development in Information Retrieval, {SIGIR} 2022, Madrid,
               Spain, July 11-15, 2022.},
  year      = {2022},
}

Dataset

We provide three processed datasets: Book-Crossing, MovieLens-1M, and Last.FM.

We follow the paper " Ripplenet: Propagating user preferences on the knowledge graph for recommender systems." to process data.

| | | Book-Crossing | MovieLens-1M | Last.FM | | :-------------------: | :------------ | ----------: | --------: | ---------: | | User-Item Interaction | #Users | 17,860 | 6,036 | 1,872 | | | #Items | 14,967 | 2,445 | 3,846 | | | #Interactions | 139,746 | 753,772 | 42,346| | Knowledge Graph | #Entities | 77,903 | 182,011| 9,366 | | | #Relations | 25 | 12| 60 | | | #Triplets | 151,500 | 1,241,996| 15,518 |

Reference

• We partially use the codes of KGIN.
• You could find all other baselines in Github.