Research map / Graph learning / Knowledge and temporal graphs
Unfolding Temporal Dynamics: Predicting Social Media Popularity Using Multi-scale Temporal Decomposition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add multi-scale time-sensitive sub-matrices to context MF (MTD vs PreferenceMF) | PreferenceMF · 750K UsD | Spearman correlation: 0.2816 -> 0.3491 | Helped |
| add multi-scale time-sensitive sub-matrices to context MF (MTD vs PreferenceMF) | PreferenceMF · 540K UmD | Spearman correlation: 0.7532 -> 0.7977 | Helped |
| add user/content similarity constraints (PreferenceMF vs BasicMF) | BasicMF · 540K UmD | Spearman correlation: 0.4822 -> 0.7532 | Helped |
| use all time scales instead of week of year only (ablation) | MTD · 750K UsD | Spearman correlation: 0.3417 -> 0.3491 | Helped |
| use all time scales instead of season of year only (ablation) | MTD · 540K UmD | Spearman correlation: 0.7468 -> 0.7977 | Helped |
| week-of-year instead of season-of-year time scale (ablation) | MTD single scale · 750K UsD | Spearman correlation: 0.3025 -> 0.3417 | Helped |
Closest papers by meaning
- SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity (CVPR 2025 · similarity 0.90)
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