Recommendation Systems on Netflix

Metric: nDCG@100 (higher is better)

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#	Model↕	nDCG@100▼	Extra Data	Paper	Date↕	Code
1	H+Vamp Gated	0.40861	No	Enhancing VAEs for Collaborative Filtering: Flex...	2019-11-03	Code
2	RecVAE	0.394	No	RecVAE: a New Variational Autoencoder for Top-N ...	2019-12-24	Code
3	EASE	0.393	No	Embarrassingly Shallow Autoencoders for Sparse D...	2019-05-08	Code
4	RaCT	0.392	No	Towards Amortized Ranking-Critical Training for ...	2019-06-10	Code
5	Mult-VAE PR	0.386	No	Variational Autoencoders for Collaborative Filte...	2018-02-16	Code
6	Mult-DAE	0.38	No	Variational Autoencoders for Collaborative Filte...	2018-02-16	Code
7	∞-AE	0.3659	No	Infinite Recommendation Networks: A Data-Centric...	2022-06-03	Code

#1H+Vamp GatedSOTA
0.40861
nDCG@100· 2019-11-03
Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms Code
#2RecVAE
0.394
nDCG@100· 2019-12-24
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback Code
#3EASESOTA
0.393
nDCG@100· 2019-05-08
Embarrassingly Shallow Autoencoders for Sparse Data Code
#4RaCT
0.392
nDCG@100· 2019-06-10
Towards Amortized Ranking-Critical Training for Collaborative Filtering Code
#5Mult-VAE PRSOTA
0.386
nDCG@100· 2018-02-16
Variational Autoencoders for Collaborative Filtering Code
#6Mult-DAE
0.38
nDCG@100· 2018-02-16
Variational Autoencoders for Collaborative Filtering Code
#7∞-AE
0.3659
nDCG@100· 2022-06-03
Infinite Recommendation Networks: A Data-Centric Approach Code