Tensorflow implementation for Self-supervised Graph Learning for Recommendation

Related tags

Deep LearningSGL
Overview

SGL

This is our Tensorflow implementation for our SIGIR 2021 paper:

Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian,and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation, Paper in arXiv.

Environment Requirement

The code runs well under python 3.7.7. The required packages are as follows:

  • Tensorflow-gpu == 1.15.0
  • numpy == 1.19.1
  • scipy == 1.5.2
  • pandas == 1.1.1
  • cython == 0.29.21

Quick Start

Firstly, compline the evaluator of cpp implementation with the following command line:

python setup.py build_ext --inplace

If the compilation is successful, the evaluator of cpp implementation will be called automatically. Otherwise, the evaluator of python implementation will be called.

Note that the cpp implementation is much faster than python.

Further details, please refer to NeuRec

Secondly, specify dataset and recommender in configuration file NeuRec.properties.

Model specific hyperparameters are in configuration file ./conf/SGL.properties.

Some important hyperparameters (taking a 3-layer SGL-ED as example):

yelp2018 dataset

aug_type=1
reg=1e-4
embed_size=64
n_layers=3
ssl_reg=0.1
ssl_ratio=0.1
ssl_temp=0.2

amazon-book dataset

aug_type=1
reg=1e-4
embed_size=64
n_layers=3
ssl_reg=0.5
ssl_ratio=0.1
ssl_temp=0.2

ifashion dataset

aug_type=1
reg=1e-3
embed_size=64
n_layers=3
ssl_reg=0.02
ssl_ratio=0.4
ssl_temp=0.5

Finally, run main.py in IDE or with command line:

python main.py
PaSST: Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
Single cell current best practices tutorial case study for the paper:Luecken and Theis, "Current best practices in single-cell RNA-seq analysis: a tutorial"

Scripts for "Current best-practices in single-cell RNA-seq: a tutorial" This repository is complementary to the publication: M.D. Luecken, F.J. Theis,

Theis Lab 968 Dec 28, 2022
Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)

Occlusion Robust 3D face Reconstruction Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee Code for Occlusion Robust 3D Face Reconstruction

Yeongjoon 31 Dec 19, 2022
A small fun project using python OpenCV, mediapipe, and pydirectinput

Here I tried a small fun project using python OpenCV, mediapipe, and pydirectinput. Here we can control moves car game when yellow color come to right box (press key 'd') left box (press key 'a') lef

Sameh Elisha 3 Nov 17, 2022
Code for One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022)

One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning (AAAI 2022) Paper | Demo Requirements Python = 3.6 , Pytorch

FuxiVirtualHuman 84 Jan 03, 2023
Automate issue discovery for your projects against Lightning nightly and releases.

Automated Testing for Lightning EcoSystem Projects Automate issue discovery for your projects against Lightning nightly and releases. You get CPUs, Mu

Pytorch Lightning 41 Dec 24, 2022
A python implementation of Deep-Image-Analogy based on pytorch.

Deep-Image-Analogy This project is a python implementation of Deep Image Analogy.https://arxiv.org/abs/1705.01088. Some results Requirements python 3

Peng Lu 171 Dec 14, 2022
Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach

Get Fooled for the Right Reason Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness throu

Sowrya Gali 1 Apr 25, 2022
Empowering journalists and whistleblowers

Onymochat Empowering journalists and whistleblowers Onymochat is an end-to-end encrypted, decentralized, anonymous chat application. You can also host

Samrat Dutta 19 Sep 02, 2022
This code is an implementation for Singing TTS.

MLP Singer This code is an implementation for Singing TTS. The algorithm is based on the following papers: Tae, J., Kim, H., & Lee, Y. (2021). MLP Sin

Heejo You 22 Dec 23, 2022
This Deep Learning Model Predicts that from which disease you are suffering.

Deep-Learning-Project This Deep Learning Model Predicts that from which disease you are suffering. This Project Covers the Topics of Deep Learning Int

Jai Viral Doshi 0 Jan 20, 2022
Code-free deep segmentation for computational pathology

NoCodeSeg: Deep segmentation made easy! This is the official repository for the manuscript "Code-free development and deployment of deep segmentation

André Pedersen 26 Nov 23, 2022
PyTorch implemention of ICCV'21 paper SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation

SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation This is the PyTorch implemention of ICCV'21 paper SGPA: Structure

Chen Kai 24 Dec 05, 2022
A library for implementing Decentralized Graph Neural Network algorithms.

decentralized-gnn A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes. De

Multimedia Knowledge and Social Analytics Lab 5 Nov 07, 2022
Learning from graph data using Keras

Steps to run = Download the cora dataset from this link : https://linqs.soe.ucsc.edu/data unzip the files in the folder input/cora cd code python eda

Mansar Youness 64 Nov 16, 2022
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

SimGNN ⠀⠀⠀ A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity s

Benedek Rozemberczki 534 Dec 25, 2022
Türkiye Canlı Mobese Görüntülerinde Profesyonel Nesne Takip Sistemi

Türkiye Mobese Görüntü Takip Türkiye Mobese görüntülerinde OPENCV ve Yolo ile takip sistemi Multiple Object Tracking System in Turkish Mobese with OPE

15 Dec 22, 2022
Like Dirt-Samples, but cleaned up

Clean-Samples Like Dirt-Samples, but cleaned up, with clear provenance and license info (generally a permissive creative commons licence but check the

TidalCycles 39 Nov 30, 2022
Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment.

(ACMMM 2021 Oral) SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment This repository shows two tasks: Face landmark detection and Fac

BoomStar 51 Dec 13, 2022
Using VapourSynth with super resolution models and speeding them up with TensorRT.

VSGAN-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Using NVIDIA/Torch-TensorRT combined wi

111 Jan 05, 2023