Collaborative Filtering On Netflix
المقاييس
AUC
PSP@10
Recall@10
Recall@100
nDCG@10
nDCG@100
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
| Paper Title | |||||||
|---|---|---|---|---|---|---|---|
| H+Vamp Gated | - | - | - | - | - | 0.40861 | Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms |
| RecVAE | - | - | - | - | - | 0.394 | RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback |
| EASE | - | - | - | - | - | 0.393 | Embarrassingly Shallow Autoencoders for Sparse Data |
| RaCT | - | - | - | - | - | 0.392 | Towards Amortized Ranking-Critical Training for Collaborative Filtering |
| Mult-VAE PR | - | - | - | - | - | 0.386 | Variational Autoencoders for Collaborative Filtering |
| Mult-DAE | - | - | - | - | - | 0.380 | Variational Autoencoders for Collaborative Filtering |
| ∞-AE | 0.9728 | 0.0375 | 0.2969 | 0.5088 | 0.3059 | 0.3659 | Infinite Recommendation Networks: A Data-Centric Approach |
| RATE-CSE | - | - | 0.2014 | - | - | - | Collaborative Similarity Embedding for Recommender Systems |
| 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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