CHA: Categorical Hierarchy-based Attention for Next POI Recommendation

Next Point-of-interest (POI) recommendation is a key task in improving location-related customer experiences and business operations, but yet remains challenging due to the substantial diversity of human activities and the sparsity of the check-in records available. To address these challenges, we proposed to explore the category hierarchy knowledge graph of POIs via an attention mechanism to learn the robust representations of POIs even when there is insufficient data. We also proposed a spatial-temporal decay LSTM and a Discrete Fourier Series-based periodic attention to better facilitate the capturing of the personalized behavior pattern. Extensive experiments on two commonly adopted real-world location-based social networks (LBSNs) datasets proved that the inclusion of the aforementioned modules helps to boost the performance of next and next new POI recommendation tasks significantly. Specifically, our model in general outperforms other state-of-the-art methods by a large margin.

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@article{DBLP:journals/tois/ZangHLWW22,
  author    = {Hongyu Zang and
               Dongcheng Han and
               Xin Li and
               Zhifeng Wan and
               Mingzhong Wang},
  title     = {CHA: Categorical Hierarchy-based Attention for Next {POI} Recommendation},
  journal   = {ACM Trans. Inf. Syst.},
  volume    = {40},
  number    = {1},
  pages     = {7:1--7:22},
  year      = {2022},
}