An Evaluation of Generative Adversarial Networks for Collaborative Filtering.

Last update: Jan 19, 2022

Overview

An Evaluation of Generative Adversarial Networks for Collaborative Filtering.

This repository was developed by Fernando B. Pérez Maurera. Fernando is a Ph.D. student at Politecnico di Milano.

This repository contains the source code of the following articles:

An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, and Paolo Cremonesi. Accepted to ECIR 2022.

See our website for more information on our research group. We are actively pursuing this research direction in evaluation and reproducibility, we are open to collaboration with other researchers. Follow our project on ResearchGate

This repo is divided in three folders:

evaluation-cfgan: Contains the implementation of CFGAN and the experiments presented in the article.
recsys-framework: Contains the implementation of baselines, hyper-parameter tuning, and evaluation used in the article.
pdf: Contains a copy of the submitted article and additional material

You'll find instructions to install this project and run the experiments in the
README inside evaluation-cfgan, in fact, all commands must be run inside the evaluation-cfgan folder.

You might also like...

Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation

502 Jan 3, 2023

PyTorch implementations of Generative Adversarial Networks.

This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as

13.4k Jan 8, 2023

Image Deblurring using Generative Adversarial Networks

DeblurGAN arXiv Paper Version Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. Our netwo

2.2k Jan 1, 2023

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

92 Dec 3, 2022

Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations

ODE GAN (Prototype) in PyTorch Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary

15 Feb 10, 2022

Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

StackGAN-v2 StackGAN-v1: Tensorflow implementation StackGAN-v1: Pytorch implementation Inception score evaluation Pytorch implementation for reproduci

809 Dec 16, 2022

Releases(v1.1.1-ecir-2022-camera-ready)

v1.1.1-ecir-2022-camera-ready(Jan 19, 2022)
Updates to README to include reference arXiv, github, and Zenodo links.

Updated CITATION file to refer to the PoliMi repo.

Source code(tar.gz)
Source code(zip)
v1.1.0-ecir-2022-camera-ready(Jan 5, 2022)

Source code(tar.gz)
Source code(zip)
v1.0.1-ecir-2022-camera-ready-prep(Jan 4, 2022)

Source code(tar.gz)
Source code(zip)
v1.0.0-ecir-2022-submission(Oct 22, 2021)

Source code(tar.gz)
Source code(zip)

An Evaluation of Generative Adversarial Networks for Collaborative Filtering.

Related tags

Overview

An Evaluation of Generative Adversarial Networks for Collaborative Filtering.

You might also like...

Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

PyTorch implementations of Generative Adversarial Networks.

Image Deblurring using Generative Adversarial Networks

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

Partial implementation of ODE-GAN technique from the paper Training Generative Adversarial Networks by Solving Ordinary Differential Equations

Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"

PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

Official implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

Releases(v1.1.1-ecir-2022-camera-ready)

v1.1.1-ecir-2022-camera-ready(Jan 19, 2022)

v1.1.0-ecir-2022-camera-ready(Jan 5, 2022)

v1.0.1-ecir-2022-camera-ready-prep(Jan 4, 2022)

v1.0.0-ecir-2022-submission(Oct 22, 2021)

Owner

Fernando Benjamín PÉREZ MAURERA

Implementation of the "Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos" paper.

Image Super-Resolution by Neural Texture Transfer

NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

automated systems to assist guarding corona Virus precautions for Closed Rooms (e.g. Halls, offices, etc..)

The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question IntentionClassification Benchmark for Text-to-SQL"

DCGAN LSGAN WGAN-GP DRAGAN PyTorch

Code to train models from "Paraphrastic Representations at Scale".

Source code for "Taming Visually Guided Sound Generation" (Oral at the BMVC 2021)

Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA)

Power Core Simulator!

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

Robust Partial Matching for Person Search in the Wild

The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

[ICLR 2022] DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

上海交通大学全自动抢课脚本，支持准点开抢与抢课后持续捡漏两种模式。2021/06/08更新。

Implementation of the algorithm shown in the article "Modelo de Predicción de Éxito de Canciones Basado en Descriptores de Audio"

DIT is a DTLS MitM proxy implemented in Python 3. It can intercept, manipulate and suppress datagrams between two DTLS endpoints and supports psk-based and certificate-based authentication schemes (RSA + ECC).

Implementation for Homogeneous Unbalanced Regularized Optimal Transport

A tutorial on training a DarkNet YOLOv4 model for the CrowdHuman dataset