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Collaborative Filtering On Movielens 100K

Metrics

Precision
RMSE (u1 Splits)
Recall

Results

Performance results of various models on this benchmark

Model Name
Precision
RMSE (u1 Splits)
Recall
Paper TitleRepository
GHRS0.7710.8870.799GHRS: 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-
0 of 17 row(s) selected.
Collaborative Filtering On Movielens 100K | SOTA | HyperAI