DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
This work addresses spatiotemporal activity prediction for applications such as urban planning and mobile advertising, representing an incremental advance by improving on existing methods with a novel disentanglement approach.
The paper tackles spatiotemporal activity prediction by proposing DisenHCN, a hypergraph neural network that disentangles user representations into aspects like location, time, and activity, achieving performance improvements of 14.23% to 18.10% over state-of-the-art methods on real-world datasets.
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.