python神经网络算法库_推荐系统算法库NeuRec：灵活全面、包括各种最先进的神经网络推荐模型...
NeuRec
An open source neural recommender library
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.