NeuRec: An open source neural recommender library [Bin Wu et al.]
May 16, 2020
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.
https://github.com/wubinzzu/NeuRec
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. |
| 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. |
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About The Author
Joeran Beel
Founder of Recommender-Systems.com (RS_c) and Professor of Intelligent Systems at the University of Siegen, Germany