The source code and dataset for the RecGURU paper (WSDM 2022)

Overview

RecGURU

About The Project

Source code and baselines for the RecGURU paper "RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation (WSDM 2022)"

Code Structure

RecGURU  
├── README.md                                 Read me file 
├── data_process                              Data processing methods
│   ├── __init__.py                           Package initialization file     
│   └── amazon_csv.py                         Code for processing the amazon data (in .csv format)
│   └── business_process.py                   Code for processing the collected data
│   └── item_frequency.py                     Calculate item frequency in each domain
│   └── run.sh                                Shell script to perform data processing  
├── GURU                                      Scripts for modeling, training, and testing 
│   ├── data                                  Dataloader package      
│     ├── __init__.py                         Package initialization file 
│     ├── data_loader.py                      Customized dataloaders 
│   └── tools                                 Tools such as loss function, evaluation metrics, etc.
│     ├── __init__.py                         Package initialization file
│     ├── lossfunction.py                     Customized loss functions
│     ├── metrics.py                          Evaluation metrics
│     ├── plot.py                             Plot function
│     ├── utils.py                            Other tools
│  ├── Transformer                            Transformer package
│     ├── __init__.py                         Package initialization 
│     ├── transformer.py                      transformer module
│  ├── AutoEnc4Rec.py                         Autoencoder based sequential recommender
│  ├── AutoEnc4Rec_cross.py                   Cross-domain recommender modules
│  ├── config_auto4rec.py                     Model configuration file
│  ├── gan_training.py                        Training methods of the GAN framework
│  ├── train_auto.py                          Main function for training and testing single-domain sequential recommender
│  ├── train_gan.py                           Main function for training and testing cross-domain sequential recommender
└── .gitignore                                gitignore file

Dataset

  1. The public datasets: Amazon view dataset at: https://nijianmo.github.io/amazon/index.html
  2. Collected datasets: https://drive.google.com/file/d/1NbP48emGPr80nL49oeDtPDR3R8YEfn4J/view
  3. Data processing:

Amazon dataset:

```shell
cd ../data_process
python amazon_csv.py   
```

Collected dataset

```shell
cd ../data_process
python business_process.py --rate 0.1  # portion of overlapping user = 0.1   
```

After data process, for each cross-domain scenario we have a dataset folder:

."a_domain"-"b_domain"
├── a_only.pickle         # users in domain a only
├── b_only.pickle         # users in domain b only
├── a.pickle              # all users in domain a
├── b.pickle              # all users in domain b
├── a_b.pickle            # overlapped users of domain a and b   

Note: see the code for processing details and make modifications accordingly.

Run

  1. Single-domain Methods:
    # SAS
    python train_auto.py --sas "True"
    # AutoRec (ours)
    python train_auto.py 
  2. Cross-Domain Methods:
    # RecGURU
    python train_gan.py --cross "True"
Owner
Chenglin Li
Chenglin Li
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022
CUAD

Contract Understanding Atticus Dataset This repository contains code for the Contract Understanding Atticus Dataset (CUAD), a dataset for legal contra

The Atticus Project 273 Dec 17, 2022
Fast RFC3339 compliant Python date-time library

udatetime: Fast RFC3339 compliant date-time library Handling date-times is a painful act because of the sheer endless amount of formats used by people

Simon Pirschel 235 Oct 25, 2022
Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

ForecastingNonverbalSignals This is the implementation for the paper Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative A

1 Feb 10, 2022
This repository contains code to train and render Mixture of Volumetric Primitives (MVP) models

Mixture of Volumetric Primitives -- Training and Evaluation This repository contains code to train and render Mixture of Volumetric Primitives (MVP) m

Meta Research 125 Dec 29, 2022
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

Object Pose Estimation Demo This tutorial will go through the steps necessary to perform pose estimation with a UR3 robotic arm in Unity. You’ll gain

Unity Technologies 187 Dec 24, 2022
Learning Features with Parameter-Free Layers (ICLR 2022)

Learning Features with Parameter-Free Layers (ICLR 2022) Dongyoon Han, YoungJoon Yoo, Beomyoung Kim, Byeongho Heo | Paper NAVER AI Lab, NAVER CLOVA Up

NAVER AI 65 Dec 07, 2022
wmctrl ported to Python Ctypes

work in progress wmctrl is a command that can be used to interact with an X Window manager that is compatible with the EWMH/NetWM specification. wmctr

Iyad Ahmed 22 Dec 31, 2022
Deep Distributed Control of Port-Hamiltonian Systems

De(e)pendable Distributed Control of Port-Hamiltonian Systems (DeepDisCoPH) This repository is associated to the paper [1] and it contains: The full p

Dependable Control and Decision group - EPFL 3 Aug 17, 2022
Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

CoMatch: Semi-supervised Learning with Contrastive Graph Regularization (Salesforce Research) This is a PyTorch implementation of the CoMatch paper [B

Salesforce 107 Dec 14, 2022
Data pipelines for both TensorFlow and PyTorch!

rapidnlp-datasets Data pipelines for both TensorFlow and PyTorch ! If you want to load public datasets, try: tensorflow/datasets huggingface/datasets

1 Dec 08, 2021
[arXiv'22] Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation

Panoptic NeRF Project Page | Paper | Dataset Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation Xiao Fu*, Shangzhan zhang*,

Xiao Fu 111 Dec 16, 2022
Scripts used to make and evaluate OpenAlex's concept tagging model

openalex-concept-tagging This repository contains all of the code for getting the concept tagger up and running. To learn more about where this model

OurResearch 18 Dec 09, 2022
A FAIR dataset of TCV experimental results for validating edge/divertor turbulence models.

TCV-X21 validation for divertor turbulence simulations Quick links Intro Welcome to TCV-X21. We're glad you've found us! This repository is designed t

0 Dec 18, 2021
Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation The code of: Context Decoupling Augmentation for Weakly Supervised Semanti

54 Dec 12, 2022
Repository of best practices for deep learning in Julia, inspired by fastai

FastAI Docs: Stable | Dev FastAI.jl is inspired by fastai, and is a repository of best practices for deep learning in Julia. Its goal is to easily ena

FluxML 532 Jan 02, 2023
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency This is a official implementation of the CycleContrast introduced in

13 Nov 14, 2022
TensorFlow implementation of Elastic Weight Consolidation

Elastic weight consolidation Introduction A TensorFlow implementation of elastic weight consolidation as presented in Overcoming catastrophic forgetti

James Stokes 67 Oct 11, 2022
The implemetation of Dynamic Nerual Garments proposed in Siggraph Asia 2021

DynamicNeuralGarments Introduction This repository contains the implemetation of Dynamic Nerual Garments proposed in Siggraph Asia 2021. ./GarmentMoti

42 Dec 27, 2022
Empower Sequence Labeling with Task-Aware Language Model

LM-LSTM-CRF Check Our New NER Toolkit 🚀 🚀 🚀 Inference: LightNER: inference w. models pre-trained / trained w. any following tools, efficiently. Tra

Liyuan Liu 838 Jan 05, 2023