Model List

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.

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.