Collaborative Filtering On Movielens 1M

RMSE

Results

Performance results of various models on this benchmark

Model Name	RMSE	Paper Title	Repository
CF-NADE	0.829	A Neural Autoregressive Approach to Collaborative Filtering
NNMF	0.843	Neural Network Matrix Factorization
BERT4Rec	-	BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
BST	0.8401	Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
GC-MC	0.832	Graph Convolutional Matrix Completion
FedGNN	0.848	FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation	-
GRU4Rec	-	Session-based Recommendations with Recurrent Neural Networks
SSE-PT	-	SSE-PT: Sequential Recommendation Via Personalized Transformer
Factorized EAE	0.860	Deep Models of Interactions Across Sets
SVAE	-	Sequential Variational Autoencoders for Collaborative Filtering
SASRec	-	Self-Attentive Sequential Recommendation
FedPerGNN	0.839	A federated graph neural network framework for privacy-preserving personalization
Factorization with dictionary learning	0.866	Dictionary Learning for Massive Matrix Factorization
∞-AE	-	Infinite Recommendation Networks: A Data-Centric Approach
SVD-AE	-	SVD-AE: Simple Autoencoders for Collaborative Filtering
U-CFN	0.8574	Hybrid Recommender System based on Autoencoders
LRML	-	Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
I-AutoRec	0.831	AutoRec: Autoencoders Meet Collaborative Filtering	-
IGMC	0.857	Inductive Matrix Completion Based on Graph Neural Networks
HSTU	-	Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

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