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}
}