Research map / Probabilistic and scientific modeling / Molecules and proteins
Learning Disentangled Representations for Recommendation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| macro-micro disentangled VAE (MacridVAE) instead of MultDAE/beta-MultVAE | VAE collaborative filtering · AliShop-7C | NDCG@100: 0.23923 -> 0.29148 | Helped |
| MacridVAE instead of beta-MultVAE | VAE collaborative filtering · ML-100k | Recall@20: 0.24838 -> 0.30951 | Helped |
| MacridVAE instead of MultDAE | VAE collaborative filtering · ML-20M | Recall@50: 0.53054 -> 0.52901 | Hurt |
| MacridVAE instead of MultDAE | VAE collaborative filtering · Netflix | NDCG@100: 0.37450 -> 0.37987 | Helped |
| use cosine similarity instead of inner product for prototype assignment (ablation) | MacridVAE · AliShop-7C | concept assignment balance (mode collapse) | Helped |
| macro disentanglement K=7 instead of K=1 (ablation) | MacridVAE · ML-100k | independence / performance | Helped |
Closest papers by meaning
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