RecBole has included more than 70 widely used recommendation algorithms.
In the latest version, we implement RecBole recommendation models covering general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We summarize the models in the following table:
Category | Model | Conference | Year | Paper |
---|---|---|---|---|
General recommendation | ||||
popularity | - | - | - | |
ItemKNN | TOIS | 2004 | 《Item-based top-N recommendation algorithms》 | |
BPR | UAI | 2009 | 《BPR: Bayesian Personalized Ranking from Implicit Feedback》 | |
NeuMF | WWW | 2017 | 《Neural Collaborative Filtering》 | |
ConvNCF | IJCAI | 2017 | 《Outer Product-based Neural Collaborative Filtering》 | |
DMF | IJCAI | 2017 | 《Deep Matrix Factorization Models for Recommender Systems》 | |
FISM | SIGKDD | 2013 | 《FISM: Factored Item Similarity Models for Top-N Recommender Systems》 | |
NAIS | TKDE | 2018 | 《NAIS: Neural Attentive Item Similarity Model for Recommendation》 | |
SpectralCF | RecSys | 2018 | 《Spectral Collaborative Filtering》 | |
GCMC | SIGKDD | 2018 | 《Graph Convolutional Matrix Completion》 | |
NGCF | SIGIR | 2019 | 《Neural Graph Collaborative Filtering》 | |
LightGCN | SIGIR | 2020 | 《LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation》 | |
DGCF | SIGIR | 2020 | 《Disentangled Graph Collaborative Filtering》 | |
MultiVAE | WWW | 2018 | 《Variational Autoencoders for Collaborative Filtering》 | |
MultiDAE | WWW | 2018 | 《Variational Autoencoders for Collaborative Filtering》 | |
CDAE | WSDM | 2016 | 《Collaborative denoising auto-encoders for top-n recommender systems》 | |
MacridVAE | NeurIPS | 2019 | 《Learning Disentangled Representations for Recommendation》 | |
LINE | WWW | 2015 | 《Large-scale Information Network Embedding》 | |
EASE | WWW | 2019 | 《Embarrassingly Shallow Autoencoders for Sparse Data》 | |
RaCT | ICLR | 2020 | 《RaCT: Towards Amortized Ranking-Critical Training for Collaborative Filtering.》 | |
RecVAE | WSDM | 2020 | 《RecVAE: A new variational autoencoder for Top-N recommendations with implicit feedback.》 | |
NNCF | CIKM | 2017 | 《A Neural Collaborative Filtering Model with Interaction-based Neighborhood.》 | |
ENMF | TOIS | 2020 | 《Efficient Neural Matrix Factorization without Sampling for Recommendation.》 | |
SLIMElastic | ICDM | 2011 | 《SLIM: Sparse Linear Methods for Top-N Recommender Systems》 | |
Context-aware recommendation | ||||
LR | WWW | 2007 | 《Predicting Clicks Estimating the Click-Through Rate for New Ads》 | |
FM | ICDM | 2010 | 《Factorization Machines》 | |
NFM | SIGIR | 2017 | 《Neural Factorization Machines for Sparse Predictive Analytics》 | |
DeepFM | IJCAI | 2017 | 《DeepFM A Factorization-Machine based Neural Network for CTR Prediction》 | |
xDeepFM | SIGKDD | 2018 | 《xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems》 | |
AFM | IJCAI | 2017 | 《Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks》 | |
FFM | RecSys | 2016 | 《Field-aware Factorization Machines for CTR Prediction》 | |
FwFM | WWW | 2018 | 《Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising》 | |
FNN(DNN) | ECIR | 2016 | 《Deep Learning over Multi-field Categorical Data》 | |
PNN | ICDM | 2016 | 《Product-based Neural Networks for User Response Prediction》 | |
DSSM | CIKM | 2013 | 《Learning Deep Structured Semantic Models for Web Search using Clickthrough Data》 | |
Wide&Deep | RecSys | 2016 | 《Wide & Deep Learning for Recommender Systems》 | |
DIN | SIGKDD | 2018 | 《Deep Interest Network for Click-Through Rate Prediction》 | |
DCN | ADKDD | 2017 | 《Deep & Cross Network for Ad Click Predictions》 | |
AutoInt | CIKM | 2019 | 《AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks》 | |
XGBoost | KDD | 2016 | 《XGBoost: A Scalable Tree Boosting System》 | |
LightGBM | NIPS | 2017 | 《LightGBM: A Highly Efficient Gradient Boosting Decision Tree》 | |
Sequential recommendation | ||||
FPMC | WWW | 2010 | 《Factorizing Personalized Markov Chains for Next-Basket Recommendation》 | |
Improved GRU-Rec | DLRS | 2016 | 《Improved recurrent neural networks for session-based recommendations》 | |
NARM | CIKM | 2017 | 《Neural attentive session-based recommendation》 | |
STAMP | SIGKDD | 2018 | 《STAMP: short-term attention/memory priority model for session-based recommendation.》 | |
Caser | WSDM | 2018 | 《Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding》 | |
NextItnet | WSDM | 2019 | 《A simple convolutional generative network for next item recommendation》 | |
TransRec | RecSys | 2017 | 《Translation-based Recommendation》 | |
SASRec | ICDM | 2018 | 《Self-Attentive Sequential Recommendation》 | |
BERT4Rec | CIKM | 2019 | 《BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer》 | |
SRGNN | AAAI | 2019 | 《Session-Based Recommendation with Graph Neural Networks》 | |
GCSAN | IJCAI | 2019 | 《Graph contextualized self-attention network for session-based recommendation》 | |
GRU4RecF(+feature embedding) | RecSys | 2016 | 《Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations》 | |
SASRecF(+feature embedding) | IJCAI | 2019 | 《Feature-level Deeper Self-Attention Network for Sequential Recommendation》 | |
FDSA | IJCAI | 2019 | 《Feature-level Deeper Self-Attention Network for Sequential Recommendation》 | |
S3Rec | CIKM | 2020 | 《S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization》 | |
GRU+KG Embedding | - | - | - | |
KSR | SIGIR | 2018 | 《Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks》 | |
Fossil | ICDM | 2016 | 《Fusing similarity models with Markov chains for sparse sequential recommendation》 | |
RepeatNet | AAAI | 2019 | 《A Repeat Aware Neural Recommendation Machine for Session-based Recommendation》 | |
SHAN | IJCAI | 2018 | 《Sequential Recommender System based on Hierarchical Attention Network》 | |
NPE | IJCAI | 2018 | 《Neural Personalized Embedding for Collaborative Filtering》 | |
HRM | SIGIR | 2015 | 《Learning Hierarchical Representation Model for Next Basket Recommendation》 | |
HGN | SIGKDD | 2019 | 《Hierarchical Gating Networks for Sequential Recommendation》 | |
Knowledge-based recommendation | ||||
CKE | SIGKDD | 2016 | 《Collaborative Knowledge Base Embedding for Recommender Systems》 | |
CFKG | MDPI | 2018 | 《Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation》 | |
KTUP | WWW | 2019 | 《Unifying Knowledge Graph Learning and Recommendation:Towards a Better Understanding of User Preferences》 | |
KGAT | SIGKDD | 2019 | 《KGAT Knowledge Graph Attention Network for Recommendation》 | |
RippleNet | CIKM | 2018 | 《RippleNet Propagating User Preferences on the Knowledge Graph for Recommender Systems.》 | |
MKR | WWW | 2019 | 《Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation》 | |
KGCN | WWW | 2019 | 《Knowledge graph convolution networks for recommender systems》 | |
KGNN-LS | SIGKDD | 2019 | 《Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems》 |
NOTE: For some recommendation algorithms, we cannot find the original implementation, and we implemented them according to our understanding of the original paper. Besides, parameter tuning or learning algorithm may not be the optimal way as originally expected. If this case was found, please contact us to help improve the algorithm implementation.
We constructed preliminary experiments to test the time and memory cost on three different-sized datasets (small, medium and large). For detailed information, you can click the following links.
NOTE: Our test results only gave the approximate time and memory cost of our implementations in the RecBole library (based on our machine server). Any feedback or suggestions about the implementations and test are welcome. We will keep improving our implementations, and update these test results.