Collaborative Filtering On Movielens 1M
Metrics
RMSE
Results
Performance results of various models on this benchmark
Comparison Table
Model Name | RMSE |
---|---|
a-neural-autoregressive-approach-to | 0.829 |
neural-network-matrix-factorization | 0.843 |
bert4rec-sequential-recommendation-with | - |
behavior-sequence-transformer-for-e-commerce | 0.8401 |
graph-convolutional-matrix-completion | 0.832 |
fedgnn-federated-graph-neural-network-for | 0.848 |
session-based-recommendations-with-recurrent | - |
sse-pt-sequential-recommendation-via | - |
deep-models-of-interactions-across-sets | 0.860 |
sequential-variational-autoencoders-for | - |
180809781 | - |
a-federated-graph-neural-network-framework | 0.839 |
dictionary-learning-for-massive-matrix | 0.866 |
infinite-recommendation-networks-a-data | - |
svd-ae-simple-autoencoders-for-collaborative | - |
hybrid-recommender-system-based-on | 0.8574 |
latent-relational-metric-learning-via-memory | - |
autorec-autoencoders-meet-collaborative | 0.831 |
inductive-graph-pattern-learning-for | 0.857 |
actions-speak-louder-than-words-trillion | - |
hyperbolic-recommender-systems | - |
ghrs-graph-based-hybrid-recommendation-system | 0.838 |
collaborative-metric-learning | - |
kernelized-synaptic-weight-matrices | 0.824 |
unifying-knowledge-graph-learning-and | - |
glocal-k-global-and-local-kernels-for | 0.8227 |
hybrid-recommender-system-based-on | 0.8321 |
inductive-matrix-completion-using-graph | 0.829 |
context-aware-compilation-of-dnn-training | - |
explainable-knowledge-graph-based | - |
efficient-retrieval-with-learned-similarities | - |