Research map / Probabilistic and scientific modeling / Molecules and proteins
Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add metadata integration (ablation) | GDMF · Five datasets | Precision@50: 1.2% | Helped |
| add metadata integration (ablation) | GDMF · Yelp-Active | NDCG: 0.416 -> 0.494 | Helped |
| add metadata integration (ablation) | GDMF · Netflix-Full | AUC: 0.9320 -> 0.9321 | No clear effect |
| add metadata integration (ablation) | GDMF · LFM-Tracks | NDCG: 0.275 -> 0.310 | Helped |
| use GDMF instead of DCPF | dynamic PF · Five datasets | Precision@50: 5% | Helped |
| use GDMF instead of dPF | dynamic PF · Five datasets | Precision@50: 4.6% | Helped |
| use dynamic models instead of static (HPF, HCPF) | recommender · All datasets | Precision@50 and Recall@50 | Helped |
| use mGDMF instead of baselines on long-tailed data | mGDMF · Netflix-Full | Precision@50: 10% | Helped |
| use mGDMF instead of baselines | mGDMF · Yelp-Active | NDCG: 38.38% to 240.69% | Helped |
| use mGDMF instead of baselines | mGDMF · Yelp-Active | AUC: 2.76% to 27.12% | Helped |
| use mGDMF instead of DCPF for sparse items (<6% ratings) | mGDMF · LFM-Tracks | sparse items recommended per user: 3.1% -> 7.6% | Helped |
| use mGDMF instead of DCPF for items with <1% sparsity | mGDMF · LFM-Tracks | sparse items recommended per user: 0.2% -> 1.6% | Helped |
Closest papers by meaning
- Coupled Poisson Factorization Integrated With User/Item Metadata for Modeling Popular and Sparse Ratings in Scalable Recommendation (AAAI 2018 · similarity 0.92)
- Hierarchical Compound Poisson Factorization (ICML 2016 · similarity 0.90)
- Ordinal Non-negative Matrix Factorization for Recommendation (ICML 2020 · similarity 0.87)
- Indexable Probabilistic Matrix Factorization for Maximum Inner Product Search (AAAI 2016 · similarity 0.87)
- Bayesian Deep Collaborative Matrix Factorization (AAAI 2019 · similarity 0.86)
- Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization (ICML 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.