DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction. (arXiv:2208.06794v1 [cs.LG])

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 …

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