Collaborative Filtering On Netflix
评估指标
AUC
PSP@10
Recall@10
Recall@100
nDCG@10
nDCG@100
评测结果
各个模型在此基准测试上的表现结果
模型名称 | AUC | PSP@10 | Recall@10 | Recall@100 | nDCG@10 | nDCG@100 | Paper Title | Repository |
|---|---|---|---|---|---|---|---|---|
| ∞-AE | 0.9728 | 0.0375 | 0.2969 | 0.5088 | 0.3059 | 0.3659 | Infinite Recommendation Networks: A Data-Centric Approach | |
| RaCT | - | - | - | - | - | 0.392 | Towards Amortized Ranking-Critical Training for Collaborative Filtering | |
| RecVAE | - | - | - | - | - | 0.394 | RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback | |
| H+Vamp Gated | - | - | - | - | - | 0.40861 | Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms | |
| Mult-DAE | - | - | - | - | - | 0.380 | Variational Autoencoders for Collaborative Filtering | |
| RATE-CSE | - | - | 0.2014 | - | - | - | Collaborative Similarity Embedding for Recommender Systems | |
| EASE | - | - | - | - | - | 0.393 | Embarrassingly Shallow Autoencoders for Sparse Data | |
| Mult-VAE PR | - | - | - | - | - | 0.386 | Variational 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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