lulu0913 / VRKG4Rec

This is the PyTorch Implementation for our model VRKG4Rec (WSDM'23)
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VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation

This is the PyTorch Implementation for the paper VRKG4Rec (WSDM'23):

Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu and Han Xu. VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation.

Introduction

Virtual Relational Knowledge Graph for Recommendation (VRKG4Rec) is a knowledge-aware recommendation framework, which explicitly distinguishes the influence of different relations for item representation learning and design a local weighted smoothing (LWS) mechanism for user and item encoding.

Citation

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

@inproceedings{10.1145/3539597.3570482,
author = {Lu, Lingyun and Wang, Bang and Zhang, Zizhuo and Liu, Shenghao and Xu, Han},
title = {VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation},
year = {2023},
isbn = {9781450394079},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/3539597.3570482},
doi = {10.1145/3539597.3570482},
booktitle = {Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining},
series = {WSDM '23}
}

Environment Requirement

The code has been tested running under Python 3.8.0. The required packages are as follows:

Usage

The instruction of commands has been clearly stated in the codes (see the parser function in utils/parser.py).

python main.py --dataset last-fm --lr 0.0001 --n_virtual 3 --context_hops 2 --n_iter 3
python main.py --dataset MovieLens --lr 0.0001 --n_virtual 3 --context_hops 2 --n_iter 3

Dataset

We provide three processed datasets: Last-FM and MovieLens.

Last-FM MovieLens
User-Item Interaction #Users 1,872 6,036
#Items 3,915 2,347
#Interactions 42,346 753,772
Knowledge Graph #Entities 9,366 6,729
#Relations 60 7
#Triplets 15,518 20,195