Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning

This is our Pytorch implementation for the paper:

Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, and Dangyang Chen (2022). Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning Paper in Arxiv, In CIKM 2022

Introduction

Knowledge-aware Recommender System with Multi-level Interactive Contrastive Learning (KGIC) is a knowledge-aware recommendation solution based on GNN and Contrastive Learning. KGIC combines multi-order CF with KG to construct local and non-local graphs for fully exploring external knowledge, and proposes a multi-level interactive contrastive mechanism tailored for knowledge-aware recommendation (intra- and inter-graph levels) for a sufficient and coherent information utilization in CF and KG.

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
• sklearn == 0.20.0

Usage

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

• Train and Test

python main.py 

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 |

Citation

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

@inproceedings{KGIC2022,
  title     = {Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning},
  author    = {
               Zou, Ding and 
               Wei, Wei and 
               Wang, Ziyang and
               Mao, Xian-Ling and
               Zhu, Feida and 
               Fang, Rui and 
               Chen, Dangyang},
  booktitle = {CIKM},
  year      = {2022}
}