GNN-based Recommendation Benchmark

Related tags

Deep Learning GRecX
Overview

GRecX

A Fair Benchmark for GNN-based Recommendation

Homepage and Documentation

Preliminary Comparison

LightGCN-Yelp dataset (featureless)

  • BCE-loss
Algo nDCG@5 nDCG@10 nDCG@15 nDCG@20
MF 0.031168 0.033510 0.037817 0.042061 (epoch:1300)
our-lightGCN 0.034872 0.037350 0.041520 0.045872 (epoch:1300)
  • BPR-loss
Algo nDCG@5 nDCG@10 nDCG@15 nDCG@20
MF 0.034672 0.037321 0.041864 0.046112
our-lightGCN 0.040223 0.042649 0.047568 0.052489 (epoch:1540)

LightGCN-Gowalla dataset (featureless)

  • BCE-loss
Algo nDCG@5 nDCG@10 nDCG@15 nDCG@20
MF --- --- --- 0.1298
our-lightGCN --- --- --- 0.1300
  • BPR-loss
Algo nDCG@5 nDCG@10 nDCG@15 nDCG@20
MF 0.116182 0.117339 0.123564 0.1400
our-lightGCN --- --- --- 0.1485

LightGCN-Amazon-book dataset (featureless)

Algo nDCG@20
lightGCN ---

Cite

If you use GRecX in a scientific publication, we would appreciate citations to the following paper:

@misc{cai2021grecx,
title={GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation},
author={Desheng Cai and Jun Hu and Shengsheng Qian and Quan Fang and Quan Zhao and Changsheng Xu},
year={2021},
eprint={2111.10342},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
You might also like...
Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).
Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

Deep Text Search - AI Based Text Search & Recommendation System Deep Text Search is an AI-powered multilingual text search and recommendation engine w

null 19 Sep 29, 2022
Code for KDD'20
Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

Heterogeneous INteract and aggreGatE (GraphHINGE) This is a pytorch implementation of GraphHINGE model. This is the experiment code in the following w

Jinjiarui 69 Nov 24, 2022
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.

COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype

Xin Xia 42 Dec 9, 2022
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

null 730 Jan 9, 2023
Deep Learning Based Fasion Recommendation System for Ecommerce
Deep Learning Based Fasion Recommendation System for Ecommerce

Project Name: Fasion Recommendation System for Ecommerce A Deep learning based streamlit web app which can recommened you various types of fasion prod

BAPPY AHMED 13 Dec 13, 2022
Official public repository of paper
Official public repository of paper "Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation"

Intention Adaptive Graph Neural Network (IAGNN) This is the official repository of paper Intention Adaptive Graph Neural Network for Category-Aware Se

null 9 Nov 22, 2022
Session-based Recommendation, CoHHN, price preferences, interest preferences, Heterogeneous Hypergraph, Co-guided Learning, SIGIR2022

This is our implementation for the paper: Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation Xiaokun Zhang, Bo

Xiaokun Zhang 27 Dec 2, 2022
[SIGIR22] Official PyTorch implementation for
[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

CORE This is the official PyTorch implementation for the paper: Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao. CORE: Simple and Effective Sess

RUCAIBox 26 Dec 19, 2022
Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 short.

Session-aware BERT4Rec Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 shor

Jamie J. Seol 22 Dec 13, 2022
Comments
  • Hyperparameters tuning.

    Hyperparameters tuning.

    Hi, Thanks for making the code public! I have a single question about hyperparameters tuning.. In the original paper both NGCF and LightGCN, the authors mentioned that they split the data via train/valid/test. But there are no validation data in the author's Github code. Also, I could not find any validation data in your code.

    So, here is the question. How do you conduct hyperparameters tuning without any validation datasets? If you use a test dataset to tune hyperparameters, I think that is unfair. If you do not agree with this, please let me know. Best regards

    opened by MLATH 0
Owner
null
GitHub
Recommendationsystem - Movie-recommendation - matrixfactorization colloborative filtering recommendation system user

recommendationsystem matrixfactorization colloborative filtering recommendation

kunal jagdish madavi 1 Jan 1, 2022
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Soubhik Sanyal 71 Dec 30, 2022
[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"

FiGNN for CTR prediction The code and data for our paper in CIKM2019: Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Predicti

Big Data and Multi-modal Computing Group, CRIPAC 75 Dec 30, 2022
We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

Deep GNN, Shallow Sampling Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, R

Facebook Research 117 Dec 20, 2022
QA-GNN: Question Answering using Language Models and Knowledge Graphs

QA-GNN: Question Answering using Language Models and Knowledge Graphs This repo provides the source code & data of our paper: QA-GNN: Reasoning with L

Michihiro Yasunaga 434 Jan 4, 2023
Distance Encoding for GNN Design

Distance-encoding for GNN design This repository is the official PyTorch implementation of the DEGNN and DEAGNN framework reported in the paper: Dista

null 172 Nov 8, 2022
Geometric Vector Perceptron --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Code to accompany Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL T

Dror Lab 85 Dec 29, 2022
A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

null 2 Jul 25, 2022
Geometric Vector Perceptrons --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Implementation of equivariant GVP-GNNs as described in Learning from Protein Structure with Geometric Vector Perceptrons b

Dror Lab 142 Dec 29, 2022
DIR-GNN - Discovering Invariant Rationales for Graph Neural Networks

DIR-GNN "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Ying-Xin (Shirley) Wu 70 Nov 13, 2022