Collaborative Filtering On Movielens 1M

RMSE

Results

Performance results of various models on this benchmark

		Paper Title
Factorization with dictionary learning	0.866	Dictionary Learning for Massive Matrix Factorization
Factorized EAE	0.860	Deep Models of Interactions Across Sets
U-CFN	0.8574	Hybrid Recommender System based on Autoencoders
IGMC	0.857	Inductive Matrix Completion Based on Graph Neural Networks
FedGNN	0.848	FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
NNMF	0.843	Neural Network Matrix Factorization
BST	0.8401	Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
FedPerGNN	0.839	A federated graph neural network framework for privacy-preserving personalization
GHRS	0.838	GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation
I-CFN	0.8321	Hybrid Recommender System based on Autoencoders
GC-MC	0.832	Graph Convolutional Matrix Completion
I-AutoRec	0.831	AutoRec: Autoencoders Meet Collaborative Filtering
CF-NADE	0.829	A Neural Autoregressive Approach to Collaborative Filtering
IMC-GAE	0.829	Inductive Matrix Completion Using Graph Autoencoder
Sparse FC	0.824	Kernelized Synaptic Weight Matrices
GLocal-K	0.8227	GLocal-K: Global and Local Kernels for Recommender Systems
BERT4Rec	-	BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
GRU4Rec	-	Session-based Recommendations with Recurrent Neural Networks
SSE-PT	-	SSE-PT: Sequential Recommendation Via Personalized Transformer
SVAE	-	Sequential Variational Autoencoders for Collaborative Filtering

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