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Collaborative Filtering On Netflix

Métriques

AUC
PSP@10
Recall@10
Recall@100
nDCG@10
nDCG@100

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
AUC
PSP@10
Recall@10
Recall@100
nDCG@10
nDCG@100
Paper TitleRepository
∞-AE0.97280.03750.29690.50880.30590.3659Infinite Recommendation Networks: A Data-Centric Approach
RaCT-----0.392Towards Amortized Ranking-Critical Training for Collaborative Filtering
RecVAE-----0.394RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
H+Vamp Gated-----0.40861Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms
Mult-DAE-----0.380Variational Autoencoders for Collaborative Filtering
RATE-CSE--0.2014---Collaborative Similarity Embedding for Recommender Systems
EASE-----0.393Embarrassingly Shallow Autoencoders for Sparse Data
Mult-VAE PR-----0.386Variational Autoencoders for Collaborative Filtering
CML--0.4612-0.2948-Collaborative Metric Learning
LRML--0.5371-0.3578-Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
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