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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
GHRS0.7710.8870.799GHRS: 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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Collaborative Filtering On Movielens 100K | SOTA | HyperAI