PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

Related tags

Deep Learningpytorchcollaborative-filteringrecsysrecommender-systempytorch-implementations
Overview

Recommender System in PyTorch

Implementations of various top-N recommender systems in PyTorch for practice.

Movielens 100k & 1M are used as datasets.

Available models

Model Paper
BPRMF Steffen Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009. Link
ItemKNN Jun Wang et al., Unifying user-based and item-based collaborative filtering approaches by similarity fusion. SIGIR 2006. Link
PureSVD Paolo Cremonesi et al., Performance of Recommender Algorithms on Top-N Recommendation Tasks. RecSys 2010. Link
SLIM Xia Ning et al., SLIM: Sparse Linear Methods for Top-N Recommender Systems. ICDM 2011. Link
P3a Colin Cooper et al., Random Walks in Recommender Systems: Exact Computation and Simulations. WWW 2014. Link
RP3b Bibek Paudel et al., Updatable, accurate, diverse, and scalablerecommendations for interactive applications. TiiS 2017. Link
DAE, CDAE Yao Wu et al., Collaborative denoising auto-encoders for top-n recommender systems. WSDM 2016.Link
MultVAE Dawen Liang et al., Variational Autoencoders for Collaborative Filtering. WWW 2018. Link
EASE Harald Steck, Embarrassingly Shallow Autoencoders for Sparse Data. WWW 2019. Link
NGCF Xiang Wang, et al., Neural Graph Collaborative Filtering. SIGIR 2019. Link
LightGCN Xiangnan He, et al., LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR 2020. Link

Enable C++ evaluation

To evaluate with C++ backend, you have to compile C++ and cython with the following script:

python setup.py build_ext --inplace

If compiled NOT successfully, "evaluation with python backend.." will be printed in the beginning.

How to run

  1. Edit experiment configurations in config.py
  2. Edit model hyperparameters you choose in conf/[MODEL_NAME]
  3. run main.py

Implement your own model

You can add your own model into the framework if:

  1. Your model inherits BaseModel class in models/BaseModel.py
  2. Implement necessary methods and add additional methods if you want.
  3. Make YourModel.conf file in conf
  4. Add your model in models.__init__

Reference

Some model implementations and util functions refers to these nice repositories.

Owner
Yoonki Jeong
Research Engineer at NAVER Corp.
Yoonki Jeong
GitHub Repository
Implementation of "With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition, BMVC, 2021" in PyTorch

Multimodal Temporal Context Network (MTCN) This repository implements the model proposed in the paper: Evangelos Kazakos, Jaesung Huh, Arsha Nagrani,

Evangelos Kazakos 13 Nov 24, 2022
State of the Art Neural Networks for Deep Learning

pyradox This python library helps you with implementing various state of the art neural networks in a totally customizable fashion using Tensorflow 2

Ritvik Rastogi 60 May 29, 2022
Towards the D-Optimal Online Experiment Design for Recommender Selection (KDD 2021)

Towards the D-Optimal Online Experiment Design for Recommender Selection (KDD 2021) Contact 0 Jan 11, 2022

(AAAI2020)Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing This repository contains pytorch source code for AAAI2020 oral paper: Grapy-ML

54 Aug 04, 2022
A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

HOW TO USE THIS PROJECT A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets Based on DeepLabCut toolbox, we run wit

1 Jan 10, 2022
MVP Benchmark for Multi-View Partial Point Cloud Completion and Registration

MVP Benchmark: Multi-View Partial Point Clouds for Completion and Registration [NEWS] 2021-07-12 [NEW 🎉 ] The submission on Codalab starts! 2021-07-1

PL 93 Dec 21, 2022
Trajectory Prediction with Graph-based Dual-scale Context Fusion

DSP: Trajectory Prediction with Graph-based Dual-scale Context Fusion Introduction This is the project page of the paper Lu Zhang, Peiliang Li, Jing C

HKUST Aerial Robotics Group 103 Jan 04, 2023
Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"

The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami   ·   Rayhane Mama   ·   Ragavan Thurairatn

Rayhane Mama 144 Dec 23, 2022
git《FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding》(CVPR 2021) GitHub: [fig8]

FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding (CVPR 2021) This repo contains the implementation of our state-of-the-art fewshot ob

233 Dec 29, 2022
Implementation of Google Brain's WaveGrad high-fidelity vocoder

WaveGrad Implementation (PyTorch) of Google Brain's high-fidelity WaveGrad vocoder (paper). First implementation on GitHub with high-quality generatio

Ivan Vovk 363 Dec 27, 2022
Simple-System-Convert--C--F - Simple System Convert With Python

Simple-System-Convert--C--F REQUIREMENTS Python version : 3 HOW TO USE Run the c

Jonathan Santos 2 Feb 16, 2022
Bald-to-Hairy Translation Using CycleGAN

GANiry: Bald-to-Hairy Translation Using CycleGAN Official PyTorch implementation of GANiry. GANiry: Bald-to-Hairy Translation Using CycleGAN, Fidan Sa

Fidan Samet 10 Oct 27, 2022
CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

Frederick Wang 3 Apr 26, 2022
PiRank: Learning to Rank via Differentiable Sorting

PiRank: Learning to Rank via Differentiable Sorting This repository provides a reference implementation for learning PiRank-based models as described

54 Dec 17, 2022
SemiNAS: Semi-Supervised Neural Architecture Search

SemiNAS: Semi-Supervised Neural Architecture Search This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian

Renqian Luo 21 Aug 31, 2022
scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

scikit-learn 52.5k Jan 08, 2023
Tello Drone Trajectory Tracking

With this library you can track the trajectory of your tello drone or swarm of drones in real time.

Kamran Asgarov 2 Oct 12, 2022
RCT-ART is an NLP pipeline built with spaCy for converting clinical trial result sentences into tables through jointly extracting intervention, outcome and outcome measure entities and their relations.

Randomised controlled trial abstract result tabulator RCT-ART is an NLP pipeline built with spaCy for converting clinical trial result sentences into

2 Sep 16, 2022
SEC'21: Sparse Bitmap Compression for Memory-Efficient Training onthe Edge

Training Deep Learning Models on The Edge Training on the Edge enables continuous learning from new data for deployed neural networks on memory-constr

Brown University Scale Lab 4 Nov 18, 2022
Flexible Option Learning - NeurIPS 2021

Flexible Option Learning This repository contains code for the paper Flexible Option Learning presented as a Spotlight at NeurIPS 2021. The implementa

Martin Klissarov 7 Nov 09, 2022