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Collaborative Filtering On Movielens 20M

評価指標

HR@10
nDCG@10

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
HR@10
nDCG@10
Paper TitleRepository
LRML0.84470.6152Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking-
HyperML0.87360.6404HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems-
GRU4Rec--Session-based Recommendations with Recurrent Neural Networks-
KGNN-LS--Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems-
HGN-0.1195Hierarchical Gating Networks for Sequential Recommendation-
EASE--Embarrassingly Shallow Autoencoders for Sparse Data-
SASRec--Self-Attentive Sequential Recommendation-
RaCT--Towards Amortized Ranking-Critical Training for Collaborative Filtering-
Mult-DAE--Variational Autoencoders for Collaborative Filtering-
RecVAE--RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback-
BERT4Rec--BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer-
Mult-VAE PR--Variational Autoencoders for Collaborative Filtering-
HSTU--Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations-
CML0.77640.5301Collaborative Metric Learning
H+Vamp Gated--Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms-
LED--Lightweight representation learning for efficient and scalable recommendation-
Multi-Gradient Descent--Multi-Gradient Descent for Multi-Objective Recommender Systems-
VASP--Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)-
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Collaborative Filtering On Movielens 20M | SOTA | HyperAI超神経