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Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add time- and distance-specific transition matrices with local window to RNN | RNN · Gowalla | MAP: 0.0921 -> 0.1038 | Helped |
| add time- and distance-specific transition matrices with local window to RNN | RNN · Gowalla | AUC: 0.7875 -> 0.8115 | Helped |
| add time- and distance-specific transition matrices with local window to RNN | RNN · GTD | MAP: 0.2600 -> 0.3238 | Helped |
| add time- and distance-specific transition matrices with local window to RNN | RNN · GTD | AUC: 0.8470 -> 0.9042 | Helped |
| increase window width from 6h to 2d (ablation) | ST-RNN · Gowalla | MAP: 0.1038 -> 0.0953 | Hurt |
| window width 1m vs 3m (ablation) | ST-RNN · GTD | recall@1 vs MAP: recall@1 0.1717 vs 0.1654; MAP 0.3175 vs 0.3238 | Mixed |
| vary embedding dimensionality (ablation) | ST-RNN · Gowalla, GTD | MAP | No clear effect |
Closest papers by meaning
- An Attentional Recurrent Neural Network for Personalized Next Location Recommendation (AAAI 2020 · similarity 0.86)
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- ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data (AAAI 2025 · similarity 0.84)
- STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM (AAAI 2025 · similarity 0.84)
- GeoMamba: Towards Multi-granular POI Recommendation with Geographical State Space Model (AAAI 2025 · similarity 0.83)
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