Location prediction forecasts a user's location based on historical user mobility traces. To tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts have been shown as significantly useful. Existing solutions mostly incorporate spatiotemporal distances between locations in mobility traces, either by feeding them as additional inputs to Recurrent Neural Networks (RNNs) or by using them to search for informative past hidden states for prediction. However, such distance-based methods fail to capture the time-varying temporal regularities of human mobility, where human mobility is often more regular in the morning than in other periods, for example; this suggests the usefulness of the actual timestamps besides the temporal distances. Against this background, we propose REPLAY, a general RNN architecture learning to capture the time-varying temporal regularities for location prediction. Specifically, REPLAY not only resorts to the spatiotemporal distances in sparse trajectories to search for the informative past hidden states, but also accommodates the time-varying temporal regularities by incorporating smoothed timestamp embeddings using Gaussian weighted averaging with timestamp-specific learnable bandwidths, which can flexibly adapt to the temporal regularities of different strengths across different timestamps. Our extensive evaluation compares REPLAY against a sizable collection of state-of-the-art techniques on two real-world datasets. Results show that REPLAY consistently and significantly outperforms state-of-the-art methods by 7.7\%-10.9\% in the location prediction task, and the bandwidths reveal interesting patterns of the time-varying temporal regularities.

翻译：位置预测基于用户历史移动轨迹预测其未来位置。为解决真实用户移动轨迹固有的稀疏性问题，时空上下文已被证明具有显著作用。现有解决方案大多通过将移动轨迹中位置间的时空距离作为循环神经网络的额外输入，或利用这些距离搜索有效历史隐藏状态进行预测。然而，此类基于距离的方法未能捕捉人类移动的时变时间规律——例如，人类移动行为在早晨通常比其它时段更具规律性；这表明除时间距离外，实际时间戳本身也具有重要价值。基于此，我们提出REPLAY——一种通用的循环神经网络架构，旨在学习捕捉时变时间规律以实现位置预测。具体而言，REPLAY不仅利用稀疏轨迹中的时空距离搜索有效历史隐藏状态，还通过引入高斯加权平均（结合时间戳特定的可学习带宽）生成平滑时间戳嵌入，从而适应不同时间戳上强度各异的时间规律。我们在两个真实数据集上将REPLAY与一系列前沿技术进行对比评估。结果表明，REPLAY在位置预测任务中稳定且显著优于现有方法（提升7.7%-10.9%），同时带宽参数揭示了时变时间规律的有趣模式。