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Collaborative Filtering On Movielens 1M
Collaborative Filtering On Movielens 1M
Metriken
RMSE
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
RMSE
Paper Title
Factorization with dictionary learning
0.866
Dictionary Learning for Massive Matrix Factorization
Factorized EAE
0.860
Deep Models of Interactions Across Sets
U-CFN
0.8574
Hybrid Recommender System based on Autoencoders
IGMC
0.857
Inductive Matrix Completion Based on Graph Neural Networks
FedGNN
0.848
FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation
NNMF
0.843
Neural Network Matrix Factorization
BST
0.8401
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
FedPerGNN
0.839
A federated graph neural network framework for privacy-preserving personalization
GHRS
0.838
GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation
I-CFN
0.8321
Hybrid Recommender System based on Autoencoders
GC-MC
0.832
Graph Convolutional Matrix Completion
I-AutoRec
0.831
AutoRec: Autoencoders Meet Collaborative Filtering
CF-NADE
0.829
A Neural Autoregressive Approach to Collaborative Filtering
IMC-GAE
0.829
Inductive Matrix Completion Using Graph Autoencoder
Sparse FC
0.824
Kernelized Synaptic Weight Matrices
GLocal-K
0.8227
GLocal-K: Global and Local Kernels for Recommender Systems
BERT4Rec
-
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
GRU4Rec
-
Session-based Recommendations with Recurrent Neural Networks
SSE-PT
-
SSE-PT: Sequential Recommendation Via Personalized Transformer
SVAE
-
Sequential Variational Autoencoders for Collaborative Filtering
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Collaborative Filtering On Movielens 1M | SOTA | HyperAI