HyperAIHyperAI
الرئيسيةالأخبارالأوراق البحثيةالدروسمجموعات البياناتالموسوعةSOTAنماذج LLMلوحة الأداء GPUالفعاليات
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HyperAIHyperAI

Collaborative Filtering On Movielens 100K

المقاييس

Precision
RMSE (u1 Splits)
Recall

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
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
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