Collaborative Filtering On Movielens 10M

Métriques

RMSE

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
U-RBM	0.823	On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
FedGNN	0.803	FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
Factorization with dictionary learning	0.799	Dictionary Learning for Massive Matrix Factorization
U-CFN	0.7954	Hybrid Recommender System based on Autoencoders
FedPerGNN	0.793	A federated graph neural network framework for privacy-preserving personalization
I-AutoRec	0.782	AutoRec: Autoencoders Meet Collaborative Filtering
GC-MC	0.777	Graph Convolutional Matrix Completion
I-CFN	0.7767	Hybrid Recommender System based on Autoencoders
SGD MF	0.772	On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
CF-NADE	0.771	A Neural Autoregressive Approach to Collaborative Filtering
Sparse FC	0.769	Kernelized Synaptic Weight Matrices
MRMA	0.7634	Mixture-Rank Matrix Approximation for Collaborative Filtering
Bayesian SVD++	0.7563	On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
Bayesian timeSVD++	0.7523	On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
Bayesian timeSVD++ flipped	0.7485	On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
SVD-AE	-	SVD-AE: Simple Autoencoders for Collaborative Filtering
scaled-CER	-	The complementarity of a diverse range of deep learning features extracted from video content for video recommendation

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Collaborative Filtering On Movielens 10M | SOTA | HyperAI