Model

RecBole has included more than 100 widely used recommendation algorithms.


Model list

In the latest version, we implement RecBole recommendation models covering general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. In addition, eight subpackage toolkits in version 2.0 based on RecBole framework have implemented 65 recommendation system models.

We summarize all 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》
SGL SIGIR 2021 《SGL: Self-supervised Graph Learning for Recommendation》
ADMM SLIM WSDM 2020 《ADMM SLIM: Sparse Recommendations for Many Users》
NCEPLRec SIGIR 2019 《Noise Contrastive Estimation for One-Class Collaborative Filtering》
SimpleX CIKM 2021 《SimpleX: A Simple and Strong Baseline for Collaborative Filtering》
NCL WWW 2022 《Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning》
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》
DIEN AAAI 2019 《Deep Interest Evolution Network for Click-Through Rate Prediction》
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) - - -
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》
LightSANs SIGIR 2021 《Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation》
SINE WSDM 2021 《Sparse-Interest Network for Sequential Recommendation》
CORE SIGIR 2022 《CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space》
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》
RecBole-DA
CL4SRec arXiv 2020 《Contrastive Learning for Sequential Recommendation》
DuoRec WSDM 2022 《Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation》
MMInfoRec ICDM 2021 《Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation》
CCL CIKM 2021 《Contrastive Curriculum Learning for Sequential User Behavior Modeling via Data Augmentation》
RecBole-MetaRec
MeLU SIGKDD 2019 《MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation》
MAMO SIGKDD 2020 《MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation》
TaNP WWW 2021 《Task-adaptive Neural Process for User Cold-Start Recommendation》
LWA NIPS 2017 《A Meta-Learning Perspective on Cold-Start Recommendations for Items》
NLBA NIPS 2017 《A Meta-Learning Perspective on Cold-Start Recommendations for Items》
MetaEmb SIGIR 2019 《Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings》
MWUF SIGIR 2021 《Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks》
RecBole-Debias
MF Computer 2009 《Matrix Factorization Techniques for Recommender Systems》
MF-IPS ICML 2016 《Recommendations as Treatments: Debiasing Learning and Evaluation》
PDA SIGIR 2021 《Causal Intervention for Leveraging Popularity Bias in Recommendation》
MACR SIGKDD 2021 《Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System》
DICE WWW 2021 《Disentangling User Interest and Conformity for Recommendation with Causal Embedding》
CausE RecSys 2018 《Causal Embeddings for Recommendation》
Rel-MF WSDM 2020 《Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback》
RecBole-FairRec
FOCF NIPS 2017 《Beyond Parity: Fairness Objectives for Collaborative Filtering》
PFCN SIGIR 2021 《Towards Personalized Fairness based on Causal Notion》
FairGo WWW 2021 《Learning Fair Representations for Recommendation: A Graph-based Perspective》
NFCF WWW 2021 《Debiasing Career Recommendations with Neural fair Collaborative Filtering》
RecBole-CDR
CMF SIGKDD 2008 《Relational Learning via Collective Matrix Factorization》
DTCDR CIKM 2019 《DTCDR: A Framework for Dual-Target Cross-Domain Recommendation》
CoNet CIKM 2018 《CoNet: Collaborative Cross Networks for Cross-Domain Recommendation》
BiTGCF CIKM 2020 《Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks》
CLFM PKDD 2013 《Cross-Domain Recommendation via Cluster-Level Latent Factor Model》
DeepAPF IJCAI 2019 《DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation》
NATR WWW 2019 《Cross-domain Recommendation Without Sharing User-relevant Data》
EMCDR IJCAI 2017 《Cross-Domain Recommendation: An Embedding and Mapping Approach》
SSCDR CIKM 2019 《Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users》
DCDCSR IJCAI 2018 《A Deep Framework for Cross-Domain and Cross-System Recommendations》
RecBole-GNN
NGCF SIGIR 2019 《Neural Graph Collaborative Filtering》
LightGCN SIGIR 2020 《LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation》
SGL SIGIR 2021 《Self-supervised Graph Learning for Recommendation》
HMLET WSDM 2022 《Linear, or Non-Linear, That is the Question!》
NCL WWW 2022 《Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning》
SimGCL SIGIR 2022 《Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation》
SR-GNN AAAI 2019 《Session-based Recommendation with Graph Neural Networks》
GC-SAN IJCAI 2019 《Graph Contextualized Self-Attention Network for Session-based Recommendation》
NISER+ CIKM 2019 《NISER: Normalized Item and Session Representations to Handle Popularity Bias》
LESSR SIGKDD 2020 《Handling Information Loss of Graph Neural Networks for Session-based Recommendation》
TAGNN SIGIR 2020 《TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation》
GCE-GNN SIGIR 2020 《Global Context Enhanced Graph Neural Networks for Session-based Recommendation》
SGNN-HN CIKM 2020 《Star Graph Neural Networks for Session-based Recommendation》
DiffNet SIGIR 2019 《A Neural Influence Diffusion Model for Social Recommendation》
MHCN WWW 2021 《Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation》
SEPT SIGKDD 2021 《Socially-Aware Self-Supervised Tri-Training for Recommendation》
RecBole-TRM
TiSASRec WSDM 2020 《Time Interval Aware Self-Attention for Sequential Recommendation》
SSE-PT RecSys 2020 《SSE-PT: Sequential Recommendation Via Personalized Transformer》
LightSANs SIGIR 2021 《Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation》
gMLP NIPS 2021 《Pay Attention to MLPs》
CORE SIGIR 2022 《CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space》
NRMS EMNLP/IJCNLP 2019 《Neural News Recommendation with Multi-Head Self-Attention》
NAML IJCAI 2019 《Neural News Recommendation with Attentive Multi-View Learning》
RecBole-PJF
BPR UAI 2009 《BPR: Bayesian Personalized Ranking from Implicit Feedback》
NeuMF WWW 2017 《Neural Collaborative Filtering》
LightGCN SIGIR 2020 《LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation》
LFRR RecSys 2019 《Latent Factor Models and Aggregation Operators for Collaborative Filtering in Reciprocal Recommender systems》
PJFNN TMIS 2018 《Person-Job fit: Adapting the Right Talent for the Right Job with Joint Representation Learning》
BPJFNN SIGIR 2018 《Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach》
APJFNN SIGIR 2018 《Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach》
BERT - - A twin tower model with a text encoder using BERT.
IPJF CIKM 2019 《Towards Effective and Interpretable Person-Job Fitting》
PJFFF CIKM 2020 《Learning Effective Representations for Person-Job Fit by Feature Fusion》
SHPJF DASFAA 2022 《Leveraging Search History for Improving Person-Job Fit》

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.


Time and memory costs

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.