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

Last update: Dec 22, 2022

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

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

Implement your own model

You can add your own model into the framework if:

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

Reference

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

NeuRec: An open source neural recommender library. Repository
RecSys 2019 - DeepLearning RS Evaluation. Paper Repository

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

Related tags

Overview

Recommender System in PyTorch

Available models

Enable C++ evaluation

How to run

Implement your own model

Reference

Owner

Yoonki Jeong

Kohei's 5th place solution for xview3 challenge

Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

ICLR2021 (Under Review)

A new version of the CIDACS-RL linkage tool suitable to a cluster computing environment.

CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator

An open source Jetson Nano baseboard and tools to design your own.

ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Keras-1D-ACGAN-Data-Augmentation

Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

TensorFlow-based implementation of "ICNet for Real-Time Semantic Segmentation on High-Resolution Images".

Ppq - A powerful offline neural network quantization tool with custimized IR

A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

Code implementation for the paper 'Conditional Gaussian PAC-Bayes'.

Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation

Official implementation of Few-Shot and Continual Learning with Attentive Independent Mechanisms

Pytorch for Segmentation

Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

Optical Character Recognition + Instance Segmentation for russian and english languages

alfred-py: A deep learning utility library for human

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

Related tags

Overview

Recommender System in PyTorch

Available models

Enable C++ evaluation

How to run

Implement your own model

Reference

Owner

Yoonki Jeong

Kohei's 5th place solution for xview3 challenge

Python scripts form performing stereo depth estimation using the CoEx model in ONNX.

ICLR2021 (Under Review)

A new version of the CIDACS-RL linkage tool suitable to a cluster computing environment.

CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator

An open source Jetson Nano baseboard and tools to design your own.

ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

Keras-1D-ACGAN-Data-Augmentation

Submodular Subset Selection for Active Domain Adaptation (ICCV 2021)

[ICCV 2021] Target Adaptive Context Aggregation for Video Scene Graph Generation

TensorFlow-based implementation of "ICNet for Real-Time Semantic Segmentation on High-Resolution Images".

Ppq - A powerful offline neural network quantization tool with custimized IR

A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

Code implementation for the paper 'Conditional Gaussian PAC-Bayes'.

Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation

Official implementation of Few-Shot and Continual Learning with Attentive Independent Mechanisms

Pytorch for Segmentation

Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

Optical Character Recognition + Instance Segmentation for russian and english languages

alfred-py: A deep learning utility library for **human**

alfred-py: A deep learning utility library for human