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Collaborative Filtering On Movielens 10M

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RMSE

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

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

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Collaborative Filtering On Movielens 10M | SOTA | HyperAI