Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling

Last update: Nov 29, 2022

Related tags

Deep Learning RHGN

Overview

RHGN

Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling

Dependencies

torch==1.6.0
torchvision==0.7.0
dgl==0.7.1
scikit-learn==0.23.2
numpy==1.19.1
scipy ==1.5.2
pandas==1.1.2

Dataset

Processed data download link: Dataset
Raw data and more details in JD-Dataset and Alibaba-dataset

Usage

For processed data:

python tb_tmain.py --data_dir ../taobao_data/ --model RHGN --label gender --graph G_ori --gpu 3  # Alibaba-Dataset

python jd_tmain.py --data_dir ../data/ --model RHGN --graph G_ori  --label age --gpu 2  # JD-Dataset

How to process data

python process.py --data_dir ../taobao_data/    #Alibaba-Dataset

python process.py --data_dir ../data/    #JD-Dataset

More command can refer run.sh and baseline.sh

Contact

[email protected]

Owner

Big Data and Multi-modal Computing Group, CRIPAC

Big Data and Multi-modal Computing Group, Center for Research on Intelligent Perception and Computing

GitHub Repository

A heterogeneous entity-augmented academic language model based on Open Academic Graph (OAG)

Library | Paper | Slack We released two versions of OAG-BERT in CogDL package. OAG-BERT is a heterogeneous entity-augmented academic language model wh

58 Dec 17, 2022

Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences forImage-Text Retrieval

NSGDC Some codes in this repo are copied/modified from opensource implementations made available by UNITER, PyTorch, HuggingFace, OpenNMT, and Nvidia.

2 Nov 07, 2022

Code for the paper 'A High Performance CRF Model for Clothes Parsing'.

Clothes Parsing Overview This code provides an implementation of the research paper: A High Performance CRF Model for Clothes Parsing Edgar Simo-S

119 Nov 21, 2022

CM building dataset Timisoara

CM_building_dataset_Timisoara Date created: Febr-2020 The Timi\c{s}oara Building Dataset - TMBuD - is composed of 160 images with the resolution of 76

5 Sep 07, 2022

Unit-Convertor - Unit Convertor Built With Python

Python Unit Converter This project can convert Weigth,length and ... units for y

1 May 31, 2022

Feature extraction made simple with torchextractor

torchextractor: PyTorch Intermediate Feature Extraction Introduction Too many times some model definitions get remorselessly copy-pasted just because

89 Oct 31, 2022

Repository to run object detection on a model trained on an autonomous driving dataset.

Autonomous Driving Object Detection on the Raspberry Pi 4 Description of Repository This repository contains code and instructions to configure the ne

51 Nov 17, 2022

functorch is a prototype of JAX-like composable function transforms for PyTorch.

1.2k Jan 09, 2023

A Japanese Medical Information Extraction Toolkit

JaMIE: a Japanese Medical Information Extraction toolkit Joint Japanese Medical Problem, Modality and Relation Recognition The Train/Test phrases requ

7 Dec 12, 2022

FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

FPGA & FreeNet Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification by Zhuo Zheng, Yanfei Zhong, Ailong M

92 Jan 03, 2023

Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image (ICCV 2021)

Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color

75 Dec 02, 2022

DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

Differentiable Model Compression via Pseudo Quantization Noise DiffQ performs differentiable quantization using pseudo quantization noise. It can auto

145 Dec 30, 2022

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

6.9k Jan 04, 2023

Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling

Related tags

Overview

RHGN

Dependencies

Dataset

Usage

For processed data:

How to process data

Contact

Owner

Big Data and Multi-modal Computing Group, CRIPAC

A heterogeneous entity-augmented academic language model based on Open Academic Graph (OAG)

Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences forImage-Text Retrieval

Code for the paper 'A High Performance CRF Model for Clothes Parsing'.

CM building dataset Timisoara

Unit-Convertor - Unit Convertor Built With Python

Feature extraction made simple with torchextractor

Repository to run object detection on a model trained on an autonomous driving dataset.

functorch is a prototype of JAX-like composable function transforms for PyTorch.

A Japanese Medical Information Extraction Toolkit

FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

Interacting Two-Hand 3D Pose and Shape Reconstruction from Single Color Image (ICCV 2021)

DiffQ performs differentiable quantization using pseudo quantization noise. It can automatically tune the number of bits used per weight or group of weights, in order to achieve a given trade-off between model size and accuracy.

HW3 ― GAN, ACGAN and UDA

Code for our ICCV 2021 Paper "OadTR: Online Action Detection with Transformers".

Balancing Principle for Unsupervised Domain Adaptation

Source code of CIKM2021 Long Paper "PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling".

An implementation of the 1. Parallel, 2. Streaming, 3. Randomized SVD using MPI4Py

A PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing"

A PyTorch implementation of the architecture of Mask RCNN

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.