NeuRec

An open source neural recommender library

Main Contributors: Bin Wu, Zhongchuan Sun, Xiangnan He, Xiang Wang, & Jonathan Staniforth

NeuRec is a comprehensive and flexible Python library for recommender systems that includes a large range of state-of-the-art neural recommender models. This library aims to solve general, social and sequential (i.e. next-item) recommendation tasks, using the Tensorflow library to provide 33 models out of the box. NeuRec is open source and available under the MIT license.

Features

Cross platform - run on any operating system with the available Docker images;
State-of-the-art - 33 neural recommender models available out of the box;
Flexible configuration - easily change the configuration settings to your exact requirements;
Easy expansion - quickly include models or datasets into NeuRec;
Fast execution - naturally support GPU, with a mutli-thread evaluator;
Detailed documentation - extensive documentation available as Jupyter notebooks in tutorial.ipynb.

Architecture

The architecture of NeuRec is shown in the diagram below:

Quick Start

Firstly, download this repository and unpack the downloaded source to a suitable location.

Secondly, go to './NeuRec' and compline the evaluator of cpp implementation with the following command line:

python setup.py build_ext --inplace

If the compilation is successful, the evaluator of cpp implementation will be called automatically. Otherwise, the evaluator of python implementation will be called.

Note that the cpp implementation is much faster than python.

Thirdly, specify dataset and recommender in configuration file NeuRec.properties.

Finally, run main.py in IDE or with command line:

python main.py

Tutorial

The short introduction to NeuRec is described in tutorial.ipynb

Models

The list of available models in NeuRec, along with their paper citations, are shown below:

General Recommender	Paper
GMF, MLP, NeuMF	Xiangnan He et al., Neural Collaborative Filtering , WWW 2017.
BPRMF	Steffen Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009.
FISM	Santosh Kabbur et al., FISM: Factored Item Similarity Models for Top-N Recommender Systems. KDD 2013.
NAIS	Xiangnan He et al., NAIS: Neural Attentive Item Similarity Model for Recommendation . TKDE2018.
DeepICF	Feng Xue et al., Deep Item-based Collaborative Filtering for Top-N Recommendation. TOIS 2019.
ConvNCF	Xiangnan He et al., Outer Product-based Neural Collaborative Filtering . IJCAI 2018.
DMF	Hong-Jian Xue et al., Deep Matrix Factorization Models for Recommender Systems. IJCAI 2017.
CDAE, DAE	Yao Wu et al., Collaborative denoising auto-encoders for top-n recommender systems. WSDM 2016.
MultiDAE, MultiVAE	Dawen Liang, et al., Variational autoencoders for collaborative filtering. WWW 2018.
JCA	Ziwei Zhu, et al., Improving Top-K Recommendation via Joint Collaborative Autoencoders. WWW 2019.
IRGAN	Jun Wang, et al., IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models. SIGIR 2017.
CFGAN	Dong-Kyu Chae, et al., CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks. CIKM 2018.
APR	Xiangnan He, et al., Adversarial Personalized Ranking for Recommendation. SIGIR 2018.
SpectralCF	Lei Zheng, et al., Spectral Collaborative Filtering. RecSys 2018.
NGCF	Xiang Wang, et al., Neural Graph Collaborative Filtering. SIGIR 2019.
WRMF	Yifan Hu, et al., Collaborative Filtering for Implicit Feedback Datasets. ICDM 2008.
LightGCN	Xiangnan He, et al., LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. SIGIR 2020.

Social Recommender	Paper
SBPR	Tong Zhao et al., Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering. CIKM 2014.
DiffNet	Le Wu et al., A Neural Influence Diffusion Model for Social Recommendation, SIGIR 2019.

Sequential Recommender	Paper
FPMC, FPMCplus	Steffen Rendle et al., Factorizing Personalized Markov Chains for Next-Basket Recommendation, WWW 2010.
HRM	Pengfei Wang et al., Learning Hierarchical Representation Model for NextBasket Recommendation, SIGIR 2015.
NPE	ThaiBinh Nguyen et al., NPE: Neural Personalized Embedding for Collaborative Filtering, ijcai 2018.
TransRec	Ruining He et al., Translation-based Recommendation, SIGIR 2015.
Caser	Jiaxi Tang et al., Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding, WSDM 2018.
Fossil	Ruining He et al., Fusing similarity models with Markov chains for sparse sequential recommendation, ICDM 2016.
GRU4Rec	Balázs Hidasi et al., Session-based Recommendations with Recurrent Neural Networks, ICLR 2016.
GRU4RecPlus	Balázs Hidasi et al., Recurrent Neural Networks with Top-k Gains for Session-based Recommendations, CIKM 2018.
SASRec	Wangcheng Kang et al., Self-Attentive Sequential Recommendation, ICDM 2018.
SRGNN	Shu Wu et al., Session-Based Recommendation with Graph Neural Networks, AAAI 2019.

Contributions

Please let us know if you experience any issues or have suggestions for new features by submitting an issue under the Issues tab or joining our WeChat Group.

Acknowledgements

The development of NeuRec is supported by the National Natural Science Foundation of China under Grant No. 61772475. This project is also supported by the National Research Foundation, Prime Minister’s Office, Singapore under its IRC@Singapore Funding Initiative.

XinyuanLu00/NeuRec