Collaborative Filtering On Movielens 100K

Metrics

Precision

RMSE (u1 Splits)

Recall

Results

Performance results of various models on this benchmark

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

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