Research map / Probabilistic and scientific modeling / Variational and generative sampling
A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| model text embeddings with vMF distribution on unit sphere instead of bag-of-words multinomial (GMove) | SHMM · Next location prediction, LA and NY | top-K accuracy: +3.2% on average | Helped |
| use low-dimensional embeddings with vMF instead of bag-of-words | SHMM · training time, 100 states | training time: 25.7% faster on LA, 40.9% faster on NY | Helped |
| increase number of hidden states (ablation) | SHMM and GMove · Next location prediction | accuracy | Helped |
| increase number of hidden states (ablation) | SHMM and GMove · Training cost | training time | Hurt |
Closest papers by meaning
- RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility (NeurIPS 2025 · similarity 0.85)
- TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond (NeurIPS 2026 · similarity 0.85)
- Generative Human Trajectory Recovery via Embedding-Space Conditional Diffusion (ICML 2025 · similarity 0.85)
- Taming the Long Tail in Human Mobility Prediction (NeurIPS 2024 · similarity 0.83)
- Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation (NeurIPS 2024 · similarity 0.83)
- Holistic Semantic Representation for Navigational Trajectory Generation (AAAI 2025 · similarity 0.82)
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