Spatiotemporal activity prediction, aiming to predict user activities at a
specific location and time, is crucial for applications like urban planning and
mobile advertising. Existing solutions based on tensor decomposition or graph
embedding suffer from the following two major limitations: 1) ignoring the
fine-grained similarities of user preferences; 2) user's modeling is entangled.
In this work, we propose a hypergraph neural network model called DisenHCN to
bridge the above gaps. In particular, we first unify the fine-grained user
similarity and the complex matching between user preferences and spatiotemporal
activity into a heterogeneous hypergraph. We then disentangle the user
representations into different aspects (location-aware, time-aware, and
activity-aware) and aggregate corresponding aspect's features on the
constructed hypergraph, capturing high-order relations from different aspects
and disentangles the impact of each aspect for final prediction. Extensive
experiments show that our DisenHCN outperforms the state-of-the-art methods by
14.23% to 18.10% on four real-world datasets. Further studies also convincingly
verify the rationality of each component in our DisenHCN.

本文介绍了一种名为 DisenHCN 的超图神经网络模型，用于预测用户在特定位置和时间的活动，该模型通过聚合不同方面的特征，捕获了不同方面的高阶关系并解开了每个方面对最终预测的影响，从而弥补了现有解决方案的不足，并在四个真实数据集上优于最先进方法 14.23％到 18.10％。