Here is a list of models and corresponding papers that have been implemented in RecStudio.

Retrievers

• Task types include General Recommendation, CTR, Sequential Recommendation, Graph-based Recommendation and Knwoledge-Graph-based Recommendation.
• Dataset types include TripletDataset, UserDataset, SeqDataset and ALSDataset.

Model Name	Task Type	Dataset Type	Published	Paper
BPR	General Recommendation	TripletDataset	UAI2009	BPR: Bayesian Personalized Ranking from Implicit Feedback
CML	General Recommendation	ALSDataset	WWW2017	Collaborative Metric Learning
DSSM	General Recommendation	TripletDataset	WWW2014	Learning Semantic Representations Using Convolutional Neural Networks for Web Search
EASE	General Recommendation	TripletDataset	WWW2019	Embarrassingly Shallow Autoencoders for Sparse Data
IRGAN	General Recommendation	ALSDataset	SIGIR2017	IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
ItemKNN	General Recommendation	TripletDataset	TOIS2004	Item-based top-N recommendation algorithms
LogisticMF	General Recommendation	TripletDataset	NIPS2014	Logistic Matrix Factorization for Implicit Feedback Data
NCF	General Recommendation	TripletDataset	WWW2017	Neural Collaborative Filtering
PMF	General Recommendation	TripletDataset	NIPS2007	Probabilistic Matrix Factorization
SLIM	General Recommendation	TripletDataset	ICDM2011	SLIM: Sparse Linear Methods for Top-N Recommender Systems
WRMF	General Recommendation	ALSDataset	ICDM2008	Collaborative Filtering for Implicit Feedback Datasets
MultiDAE	General Recommendation	UserDataset	WWW2018	Variational Autoencoders for Collaborative Filtering
MultiVAE	General Recommendation	UserDataset	WWW2018	Variational Autoencoders for Collaborative Filtering
BERT4Rec	Sequential Recommendation	SeqDataset	CIKM2019	BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Caser	Sequential Recommendation	SeqDataset	WSDM2018	Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
CL4Rec	Sequential Recommendation	SeqDataset	SIGIR2021	Contrastive Learning for Sequential Recommendation
CoSeRec	Sequential Recommendation	SeqDataset		Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
DIN	Sequential Recommendation	SeqDataset	KDD2018	Deep Interest Network for Click-Through Rate Prediction
FPMC	Sequential Recommendation	SeqDataset	WWW2010	Factorizing personalized Markov chains for next-basket recommendation
GRU4Rec	Sequential Recommendation	SeqDataset	ICLR2016	Session-Based Recommendations with Recurrent Neural Networks
HGN	Sequential Recommendation	SeqDataset	KDD2019	HGN: Hierarchical Gating Networks for Sequential Recommendation
ICLRec	Sequential Recommendation	SeqDataset	WWW2022	Intent Contrastive Learning for Sequential Recommendation
NARM	Sequential Recommendation	SeqDataset	CIKM2017	Neural Attentive Session-based Recommendation
NPE	Sequential Recommendation	SeqDataset	IJCAI2018	NPE: Neural Personalized Embedding for Collaborative Filtering
SASRec	Sequential Recommendation	SeqDataset	ICDM2018	Self-Attentive Sequential Recommendation
STAMP	Sequential Recommendation	SeqDataset	KDD2018	STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation
TransRec	Sequential Recommendation	SeqDataset	RecSys2017	Translation-based Recommendation
LightGCN	Graph-based Recommendation	TripletDataset	SIGIR2020	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
NCL	Graph-based Recommendation	TripletDataset	WWW2022	NCL: Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
NGCF	Graph-based Recommendation	TripletDataset	SIGIR2019	Neural Graph Collaborative Filtering
SGL	Graph-based Recommendation	TripletDataset	SIGIR2021	SGL: Self-supervised Graph Learning for Recommendation
SimGCL	Graph-based Recommendation	TripletDataset	SIGIR2022	Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation

Rankers

Model Name	Task Type	Dataset Type	Published	Paper
FM	CTR	TripletDataset	ICDM2010	Factorization Machines
DCN	CTR	TripletDataset	ADKDD2017	Deep & Cross Network for Ad Click Predictions
DeepFM	CTR	TripletDataset	IJCAI2017	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
LR	CTR	TripletDataset	WWW2007	Predicting Clicks: Estimating the Click-Through Rate for New Ads
NFM	CTR	TripletDataset	SIGIR2017	Neural Factorization Machines for Sparse Predictive Analytics
WideDeep	CTR	TripletDataset	DLRS2016	Wide & Deep Learning for Recommender Systems
xDeepFM	CTR	TripletDataset	KDD2018	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
DIN	CTR	SeqDataset	KDD2018	Deep Interest Network for Click-Through Rate Prediction