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Jinfeng-Xu/Awesome-Multimodal-Recommender-Systems
[TMM'26] Continuously Updated Awesome Multimodal Recommendation Paper List
96 4 +0/wk
GitHub
awesome awesome-list multimodal recommendation
Trend
0
Star & Fork Trend (14 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
Jinfeng-Xu/Awesome-Multimodal-Recommender-Systems has +0 stars this period . Velocity data will be available after more historical data is collected.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | Awesome-Multimodal-Recommender-Systems | vector-inference | Lexoid | Mammo-CLIP |
|---|---|---|---|---|
| Stars | 96 | 95 | 98 | 90 |
| Forks | 4 | 13 | 12 | 34 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | N/A | Python | Python | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | Apache-2.0 | Apache-2.0 | NOASSERTION |
Capability Radar vs vector-inference
Awesome-Multimodal-Recommender-Systems
vector-inference
Maintenance Activity 100
Last code push 1 days ago.
Community Engagement 68
Fork-to-star ratio: 4.2%. Lower fork ratio may indicate passive usage.
Issue Burden 70
Issue data not yet available.
Growth Momentum 30
No measurable growth in the current period (first-day cold start expected).
License Clarity 95
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.