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：位置预测基于历史用户移动轨迹预测用户位置。针对真实用户轨迹中固有的稀疏性问题，时空上下文已被证明具有显著价值。现有方案主要通过两种方式利用轨迹中位置间的时空距离：将其作为额外输入馈入循环神经网络，或用于搜索具有信息量的历史隐藏状态进行预测。然而，这种基于距离的方法未能捕捉人类移动性的时变时间规律——例如人类移动在上午通常比其他时段更具规律性——这表明除时间距离外，实际时间戳也具有重要价值。基于此，我们提出REPLAY——一种学习捕捉时变时间规律进行位置预测的通用循环神经网络架构。具体而言，REPLAY不仅借助稀疏轨迹中的时空距离搜索具有信息量的历史隐藏状态，还通过引入采用高斯加权平均的可学习时变带宽的平滑时间戳嵌入来适应时间规律，带宽可灵活适配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与大量先进技术进行对比，实验结果表明：REPLAY在位置预测任务中持续显著优于现有方法（提升7.7%-10.9%），且学习到的带宽揭示了时变时间规律的有趣模式。