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Collaborative Filtering On Movielens 100K

Métriques

Precision

RMSE (u1 Splits)

Recall

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Precision	RMSE (u1 Splits)	Recall	Paper Title	Repository
GHRS	0.771	0.887	0.799	GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation
Self-Supervised Exchangeable Model	-	0.91	-	Deep Models of Interactions Across Sets
IGMC	-	0.905	-	Inductive Matrix Completion Based on Graph Neural Networks
GMC	-	0.996	-	Matrix Completion on Graphs
GLocal-K	-	0.8889	-	GLocal-K: Global and Local Kernels for Recommender Systems
sRGCNN	-	0.929	-	Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
FedGNN	-	-	-	FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation	-
GraphRec + Feat	-	0.897	-	Attribute-aware non-linear co-embeddings of graph features
GC-MC	-	0.905	-	Graph Convolutional Matrix Completion
Factorized EAE	-	0.920	-	Deep Models of Interactions Across Sets
GraphRec	-	0.904	-	Attribute-aware non-linear co-embeddings of graph features
GRALS	-	0.945	-	Collaborative Filtering with Graph Information: Consistency and Scalable Methods
FedPerGNN	-	-	-	A federated graph neural network framework for privacy-preserving personalization
GRAEM / KPMF	-	0.9174	-	Scalable Probabilistic Matrix Factorization with Graph-Based Priors
GC-MC	-	0.910	-	Graph Convolutional Matrix Completion
MG-GAT	-	0.890	-	Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective
WMLFF	-	0.928	-	Weighted Multi-Level Feature Factorization for App ads CTR and installation prediction

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