Yi Feng, Su Lei, He Huaiwen, Xiao Tao
School of Computer Science, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, Guangdong Province, China.
PeerJ Comput Sci. 2024 Jan 31;10:e1851. doi: 10.7717/peerj-cs.1851. eCollection 2024.
Understanding human periodic behaviors is crucial in many applications. Existing research has shown the existence of periodicity in human behaviors, but has achieved limited success in leveraging location periodicity and obtaining satisfactory accuracy for oscillations in human periodic behaviors. In this article, we propose the Mobility Intention and Relative Entropy (MIRE) model to address these challenges. We employ tensor decomposition to extract mobility intentions from spatiotemporal datasets, thereby revealing hidden structures in users' historical records. Subsequently, we utilize subsequences associated with the same mobility intention to mine human periodic behaviors. Furthermore, we introduce a novel periodicity detection algorithm based on relative entropy. Our experimental results, conducted on real-world datasets, demonstrate the effectiveness of the MIRE model in accurately uncovering human periodic behaviors. Comparative analysis further reveals that the MIRE model significantly outperforms baseline periodicity detection algorithms.
理解人类的周期性行为在许多应用中至关重要。现有研究已表明人类行为中存在周期性，但在利用位置周期性以及获得令人满意的人类周期性行为振荡精度方面取得的成功有限。在本文中，我们提出了移动意图与相对熵（MIRE）模型来应对这些挑战。我们采用张量分解从时空数据集中提取移动意图，从而揭示用户历史记录中的隐藏结构。随后，我们利用与相同移动意图相关的子序列来挖掘人类周期性行为。此外，我们引入了一种基于相对熵的新型周期性检测算法。我们在真实世界数据集上进行的实验结果表明，MIRE模型在准确揭示人类周期性行为方面是有效的。对比分析进一步表明，MIRE模型显著优于基线周期性检测算法。
