Two versions of RecBole have implemented more than 100 commonly used recommendation algorithms.
We summarize all the models in the following table (arranged in lexicographic order after the model name is lowercase) and our web page.
| Model | Publish | Paper | Repository |
|---|---|---|---|
| ADMM SLIM | WSDM'20 | ADMM SLIM: Sparse Recommendations for Many Users | RecBole |
| AFM | IJCAI'17 | Attentional Factorization Machines: Learning the Weight of Feature Interactions via | RecBole |
| APJFNN | SIGIR'18 | Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach | RecBole-PJF |
| AutoInt | CIKM'18 | AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | RecBole |
| BERT | - | A twin tower model with a text encoder using BERT. | RecBole-PJF |
| BERT4Rec | CIKM'19 | BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer | RecBole |
| BiTGCF | CIKM'20 | Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks | RecBole-CDR |
| BPJFNN | SIGIR'18 | Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach | RecBole-PJF |
| BPR | UAI'09 | BPR: Bayesian Personalized Ranking from Implicit Feedback | RecBole |
| Caser | WSDM'18 | Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding | RecBole |
| CausE | RecSys'18 | Causal Embeddings for Recommendation | RecBole-Debias |
| CCL | CIKM'21 | Contrastive Curriculum Learning for Sequential User Behavior Modeling via Data Augmentation | RecBole-DA |
| CDAE | WSDM'16 | Collaborative Denoising Auto-encoders for Top-n Recommender Systems | RecBole |
| CFKG | MDPI'18 | Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation | RecBole |
| CKE | KDD'16 | Collaborative Knowledge Base Embedding for Recommender Systems | RecBole |
| CL4SRec | arXiv'20 | Contrastive Learning for Sequential Recommendation | RecBole-DA |
| CLFM | PKDD'13 | Cross-Domain Recommendation via Cluster-Level Latent Factor Model | RecBole-CDR |
| CMF | KDD'08 | Relational Learning via Collective Matrix Factorization | RecBole-CDR |
| CoNet | CIKM'18 | CoNet: Collaborative Cross Networks for Cross-Domain Recommendation | RecBole-CDR |
| ConvNCF | IJCAI'17 | Outer Product-based Neural Collaborative Filtering | RecBole |
| CORE | SIGIR'22 | CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space | RecBole |
| DCDCSR | IJCAI'18 | A Deep Framework for Cross-Domain and Cross-System Recommendations | RecBole-CDR |
| DCN | ADKDD'17 | Deep & Cross Network for Ad Click Predictions | RecBole |
| DCN V2 | WWW '21 | DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems | RecBole |
| DeepAPF | IJCAI'19 | DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video Recommendation | RecBole-CDR |
| DeepFM | IJCAI'17 | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | RecBole |
| DGCF | SIGIR'20 | Disentangled Graph Collaborative Filtering | RecBole |
| DICE | WWW'21 | Disentangling User Interest and Conformity for Recommendation with Causal Embedding | RecBole-Debias |
| DIEN | AAAI'19 | Deep Interest Evolution Network for Click-Through Rate Prediction | RecBole |
| DiffRec | SIGIR'23 | Diffusion Recommender Model | RecBole |
| DiffNet | SIGIR'19 | A Neural Influence Diffusion Model for Social Recommendation | RecBole-GNN |
| DIN | KDD'18 | Deep Interest Network for Click-Through Rate Prediction | RecBole |
| DMF | IJCAI'17 | Deep Matrix Factorization Models for Recommender Systems | RecBole |
| DSSM | CIKM'13 | Learning Deep Structured Semantic Models for Web Search using Clickthrough Data | RecBole |
| DTCDR | CIKM'19 | DTCDR: A Framework for Dual-Target Cross-Domain Recommendation | RecBole-CDR |
| DuoRec | WSDM'22 | Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation | RecBole-DA |
| EASE | WWW'19 | Embarrassingly Shallow Autoencoders for Sparse Data | RecBole |
| EMCDR | IJCAI'17 | Cross-Domain Recommendation: An Embedding and Mapping Approach | RecBole-CDR |
| ENMF | TOIS'20 | Efficient Neural Matrix Factorization without Sampling for Recommendation | RecBole |
| EulerNet | SIGIR'23 | EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction | RecBole |
| FairGo | WWW'21 | Learning Fair Representations for Recommendation: A Graph-based Perspective | RecBole-FairRec |
| FEARec | SIGIR'23 | Frequency Enhanced Hybrid Attention Network for Sequential Recommendation | RecBole |
| FDSA | IJCAI'19 | Feature-level Deeper Self-Attention Network for Sequential Recommendation | RecBole |
| FFM | RecSys'16 | Field-aware Factorization Machines for CTR Prediction | RecBole |
| FiGNN | CIKM '19 | Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction | RecBole |
| FISM | KDD'13 | FISM: Factored Item Similarity Models for Top-N Recommender Systems | RecBole |
| FM | ICDM'10 | Factorization Machines | RecBole |
| FNN | ECIR'16 | Deep Learning over Multi-field Categorical Data | RecBole |
| FOCF | NIPS'17 | Beyond Parity: Fairness Objectives for Collaborative Filtering | RecBole-FairRec |
| FOMeLU | KDD'19 | MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation | RecBole-MetaRec |
| FOSSIL | ICDM'16 | Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation | RecBole |
| FPMC | WWW'10 | Factorizing Personalized Markov Chains for Next-Basket Recommendation | RecBole |
| FwFM | WWW'18 | Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising | RecBole |
| GCMC | KDD'18 | Graph Convolutional Matrix Completion | RecBole |
| GCE-GNN | SIGIR'20 | Global Context Enhanced Graph Neural Networks for Session-based Recommendation | RecBole-GNN |
| GCSAN | IJCAI'19 | Graph Contextualized Self-Attention Network for Session-based Recommendation | RecBole |
| GCSAN (PyG) | IJCAI'19 | Graph Contextualized Self-Attention Network for Session-based Recommendation | RecBole-GNN |
| gMLP | NIPS'21 | Pay Attention to MLPs | RecBole-TRM |
| GRU4Rec | DLRS'16 | Improved Recurrent Neural Networks for Session-based Recommendations | RecBole |
| GRU4RecF | RecSys'16 | Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations | RecBole |
| GRU4RecKG | - | - | RecBole |
| HMLET | WSDM'22 | Linear, or Non-Linear, That is the Question! | RecBole-GNN |
| HGN | KDD'19 | Hierarchical Gating Networks for Sequential Recommendation | RecBole |
| HRM | SIGIR'15 | Learning Hierarchical Representation Model for Next Basket Recommendation | RecBole |
| IPJF | CIKM'19 | Towards Effective and Interpretable Person-Job Fitting | RecBole-PJF |
| ItemKNN | TOIS'04 | Item-based top-N Recommendation Algorithms | RecBole |
| KD_DAGFM | WSDM'23 | Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation | RecBole |
| KGAT | KDD'19 | KGAT: Knowledge Graph Attention Network for Recommendation | RecBole |
| KGCN | WWW'19 | Knowledge Graph Convolution Networks for Recommender Systems | RecBole |
| KGIN | WWW'21 | Learning Intents behind Interactions with Knowledge Graph for Recommendation | RecBole |
| KGNNLS | KDD'19 | Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems | RecBole |
| KSR | SIGIR'18 | Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks | RecBole |
| KTUP | WWW'19 | Unifying Knowledge Graph Learning and Recommendation:Towards a Better Understanding of User Preferences | RecBole |
| LDiffRec | SIGIR'23 | Diffusion Recommender Model | RecBole |
| LESSR | KDD'19 | Handling Information Loss of Graph Neural Networks for Session-based Recommendation | RecBole-GNN |
| LFRR | RecSys'19 | Latent Factor Models and Aggregation Operators for Collaborative Filtering in Reciprocal Recommender systems | RecBole-PJF |
| LightGBM | NIPS'17 | LightGBM: A Highly Efficient Gradient Boosting Decision Tree | RecBole |
| LightGCN | SIGIR'20 | LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation | RecBole |
| LightGCN (PyG) | SIGIR'20 | LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation | RecBole-GNN |
| LightSANs | SIGIR'21 | Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation | RecBole |
| LINE | WWW'15 | Large-scale Information Network Embedding | RecBole |
| LR | WWW'17 | Predicting Clicks Estimating the Click-Through Rate for New Ads | RecBole |
| LWA | NIPS'17 | A Meta-Learning Perspective on Cold-Start Recommendations for Items | RecBole-MetaRec |
| MACR | KDD'21 | Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System | RecBole-Debias |
| MacridVAE | NIPS'19 | Learning Disentangled Representations for Recommendation | RecBole |
| MAMO | KDD'20 | MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation | RecBole-MetaRec |
| MCCLK | SIGIR'22 | Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System | RecBole |
| MeLU | KDD'19 | MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation | RecBole-MetaRec |
| MetaEmb | SIGIR'19 | Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings | RecBole-MetaRec |
| MF | Computer'09 | Matrix Factorization Techniques for Recommender Systems | RecBole-Debias |
| MF-IPS | ICML'16 | Recommendations as Treatments: Debiasing Learning and Evaluation | RecBole-Debias |
| MHCN | WWW'21 | Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation | RecBole-GNN |
| MKR | WWW'19 | Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation | RecBole |
| MMInfoRec | ICDM'21 | Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation | RecBole-DA |
| MultiDAE | WWW'18 | Variational Autoencoders for Collaborative Filtering | RecBole |
| MultiVAE | WWW'18 | Variational Autoencoders for Collaborative Filtering | RecBole |
| MWUF | SIGIR'21 | Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks | RecBole-MetaRec |
| NAIS | TKDE'18 | NAIS: Neural Attentive Item Similarity Model for Recommendation | RecBole |
| NAML | IJCAI'19 | Neural News Recommendation with Attentive Multi-View Learning | RecBole-TRM |
| NARM | CIKM'17 | Neural Attentive Session-based Recommendation | RecBole |
| NATR | WWW'19 | Cross-domain Recommendation Without Sharing User-relevant Data | RecBole-CDR |
| NCE-PLRec | SIGIR'19 | Noise Contrastive Estimation for One-Class Collaborative Filtering | RecBole |
| NCL | WWW'22 | Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning | RecBole |
| NCL (PyG) | WWW'22 | Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning | RecBole-GNN |
| NeuMF | WWW'17 | Neural Collaborative Filtering | RecBole |
| NextItNet | WSDM'19 | A Simple Convolutional Generative Network for Next Item Recommendation | RecBole |
| NFCF | WWW'21 | Debiasing Career Recommendations with Neural fair Collaborative Filtering | RecBole-FairRec |
| NFM | SIGIR'17 | Neural Factorization Machines for Sparse Predictive Analytics | RecBole |
| NGCF | SIGIR'19 | Neural Graph Collaborative Filtering | RecBole |
| NGCF (PyG) | SIGIR'19 | Neural Graph Collaborative Filtering | RecBole-GNN |
| NISER+ | CIKM'19 | NISER: Normalized Item and Session Representations to Handle Popularity Bias | RecBole-GNN |
| NLBA | NIPS'17 | A Meta-Learning Perspective on Cold-Start Recommendations for Items | RecBole-MetaRec |
| NNCF | CIKM'17 | A Neural Collaborative Filtering Model with Interaction-based Neighborhood | RecBole |
| NPE | IJCAI'18 | NPE: Neural Personalized Embedding for Collaborative Filtering | RecBole |
| NRMS | EMNLP'19 | Neural News Recommendation with Multi-Head Self-Attention | RecBole-TRM |
| PDA | SIGIR'21 | Causal Intervention for Leveraging Popularity Bias in Recommendation | RecBole-Debias |
| PFCN | SIGIR'21 | Towards Personalized Fairness based on Causal Notion | RecBole-FairRec |
| PJFFF | CIKM'20 | Learning Effective Representations for Person-Job Fit by Feature Fusion | RecBole-PJF |
| PJFNN | TMIS'18 | Person-Job fit: Adapting the Right Talent for the Right Job with Joint Representation Learning | RecBole-PJF |
| Pop | - | - | RecBole |
| PNN | ICDM'16 | Product-based Neural Networks for User Response Prediction | RecBole |
| RaCT | ICLR'20 | RaCT: Towards Amortized Ranking-Critical Training for Collaborative Filtering | RecBole |
| RecVAE | WSDM'20 | RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback | RecBole |
| Rel-MF | WSDM'20 | Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback | RecBole-Debias |
| RepeatNet | AAAI'19 | RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation | RecBole |
| RippleNet | CIKM'18 | RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems | RecBole |
| S3Rec | CIKM'20 | S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization | RecBole |
| SASRec | ICDM'18 | Self-Attentive Sequential Recommendation | RecBole |
| SASRecF | - | - | RecBole |
| SEPT | KDD'21 | Socially-Aware Self-Supervised Tri-Training for Recommendation | RecBole-GNN |
| SGL | SIGIR'21 | SGL: Self-supervised Graph Learning for Recommendation | RecBole |
| SGL (PyG) | SIGIR'21 | SGL: Self-supervised Graph Learning for Recommendation | RecBole-GNN |
| SGNN-HN | CIKM'20 | Star Graph Neural Networks for Session-based Recommendation | RecBole-GNN |
| SHAN | IJCAI'18 | Sequential Recommender System based on Hierarchical Attention Network | RecBole |
| SHPJF | DASFAA'22 | Leveraging Search History for Improving Person-Job Fit | RecBole-PJF |
| SimGCL | SIGIR'22 | Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation | RecBole-GNN |
| SimpleX | CIKM'21 | SimpleX: A Simple and Strong Baseline for Collaborative Filtering | RecBole |
| SINE | WSDM'21 | Sparse-Interest Network for Sequential Recommendation | RecBole |
| SLIMElastic | ICDM'11 | SLIM: Sparse Linear Methods for Top-N Recommender Systems | RecBole |
| SpectralCF | RecSys'18 | Spectral Collaborative Filtering | RecBole |
| SRGNN | AAAI'19 | Session-based Recommendation with Graph Neural Networks | RecBole |
| SRGNN (PyG) | AAAI'19 | Session-based Recommendation with Graph Neural Networks | RecBole-GNN |
| SSCDR | CIKM'19 | Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users | RecBole-CDR |
| SSE-PT | RecSys'20 | SSE-PT: Sequential Recommendation Via Personalized Transformer | RecBole-TRM |
| STAMP | KDD'18 | STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation | RecBole |
| TAGNN | SIGIR'20 | TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation | RecBole-GNN |
| TaNP | WWW'21 | Task-adaptive Neural Process for User Cold-Start Recommendation | RecBole-MetaRec |
| TiSASRec | WSDM'20 | Time Interval Aware Self-Attention for Sequential Recommendation | RecBole-TRM |
| TransRec | RecSys'17 | Translation-based Recommendation | RecBole |
| WideDeep | RecSys'16 | Wide & Deep Learning for Recommender Systems | RecBole |
| xDeepFM | KDD'18 | xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | RecBole |
| XGBoost | KDD'16 | XGBoost: A Scalable Tree Boosting System | RecBole |