A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling
Authors: Wanzheng Zhu, Chao Zhang, Shuochao Yao, Xiaobin Gao, Jiawei Han
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have performed extensive experiments on both synthetic and real-life data. The results on synthetic data verify our theoretical analysis; while the results on real-life data demonstrate that SHMM learns meaningful semantics-rich mobility models, outperforms state-of-the-art mobility models for next location prediction, and incurs lower training cost. |
| Researcher Affiliation | Academia | Wanzheng Zhu, Chao Zhang, Shuochao Yao, Xiaobin Gao, Jiawei Han University of Illinois at Urbana-Champaign, Urbana, IL, USA {wz6, czhang82, syao9, xgao16, hanj}@illinois.edu |
| Pseudocode | No | The paper describes algorithms and procedures in narrative text and mathematical formulas but does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The real-life datasets are semantic traces from Twitter users collected by Zhang et al. (Zhang et al. 2016a). |
| Dataset Splits | No | Given a semantic trace dataset, we randomly select 70% traces for model training and use the rest 30% for testing. |
| Hardware Specification | Yes | We implemented SHMM and the baseline methods in JAVA and conducted all the experiments on a computer with 2.9 GHz Intel Core i7 CPU and 16GB memory. |
| Software Dependencies | No | The paper mentions 'JAVA' as the implementation language but does not specify a version number or list other software dependencies (e.g., libraries, frameworks) with their specific versions. |
| Experiment Setup | Yes | The parameters used are: min-count=10, size=30, window=5, sample=10 4, negative=5. |