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Models/Mult-DAE

Mult-DAE

Reported on 9 benchmarks across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Knowledge Base9 results

Recommendation SystemsonMovieLens 20M
Recall@20· 2018-02-16
0.387
best: 0.418 (Multi-Gradient Descent)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814
Recommendation SystemsonMovieLens 20M
Recall@50· 2018-02-16
0.524
best: 0.553 (RecVAE)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814
Recommendation SystemsonMovieLens 20M
nDCG@100· 2018-02-16
0.419
best: 0.448 (VASP)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814
Recommendation SystemsonMillion Song Dataset
Recall@20· 2018-02-16
0.266
best: 0.333 (EASE)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814
Recommendation SystemsonMillion Song Dataset
Recall@50· 2018-02-16
0.363
best: 0.428 (EASE)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814
Recommendation SystemsonMillion Song Dataset
nDCG@100· 2018-02-16
0.313
best: 0.389 (EASE)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814
Recommendation SystemsonNetflix
Recall@20· 2018-02-16
0.344
best: 0.37678 (H+Vamp Gated)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814
Recommendation SystemsonNetflix
Recall@50· 2018-02-16
0.438
best: 0.46252 (H+Vamp Gated)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814
Recommendation SystemsonNetflix
nDCG@100· 2018-02-16
0.38
best: 0.40861 (H+Vamp Gated)
Variational Autoencoders for Collaborative Filtering arXiv:1802.05814