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Collaborative Filtering On Movielens 1M

المقاييس

RMSE

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
RMSE
Paper TitleRepository
CF-NADE0.829A Neural Autoregressive Approach to Collaborative Filtering-
NNMF0.843Neural Network Matrix Factorization-
BERT4Rec-BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer-
BST0.8401Behavior Sequence Transformer for E-commerce Recommendation in Alibaba-
GC-MC0.832Graph Convolutional Matrix Completion-
FedGNN0.848FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation-
GRU4Rec-Session-based Recommendations with Recurrent Neural Networks-
SSE-PT-SSE-PT: Sequential Recommendation Via Personalized Transformer
Factorized EAE0.860Deep Models of Interactions Across Sets-
SVAE-Sequential Variational Autoencoders for Collaborative Filtering-
SASRec-Self-Attentive Sequential Recommendation-
FedPerGNN0.839A federated graph neural network framework for privacy-preserving personalization
Factorization with dictionary learning0.866Dictionary Learning for Massive Matrix Factorization-
∞-AE-Infinite Recommendation Networks: A Data-Centric Approach-
SVD-AE-SVD-AE: Simple Autoencoders for Collaborative Filtering-
U-CFN0.8574Hybrid Recommender System based on Autoencoders-
LRML-Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking-
I-AutoRec0.831AutoRec: Autoencoders Meet Collaborative Filtering-
IGMC0.857Inductive Matrix Completion Based on Graph Neural Networks-
HSTU-Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations-
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Collaborative Filtering On Movielens 1M | SOTA | HyperAI