Next point-of-interest (POI) recommendation is a critical task in location-based social networks, yet remains challenging due to a high degree of variation and personalization exhibited in user movements. In this work, we explore the latent hierarchical structure composed of multi-granularity short-term structural patterns in user check-in sequences. We propose a Spatio-Temporal context AggRegated Hierarchical Transformer (STAR-HiT) for next POI recommendation, which employs stacked hierarchical encoders to recursively encode the spatio-temporal context and explicitly locate subsequences of different granularities. More specifically, in each encoder, the global attention layer captures the spatio-temporal context of the sequence, while the local attention layer performed within each subsequence enhances subsequence modeling using the local context. The sequence partition layer infers positions and lengths of subsequences from the global context adaptively, such that semantics in subsequences can be well preserved. Finally, the subsequence aggregation layer fuses representations within each subsequence to form the corresponding subsequence representation, thereby generating a new sequence of higher-level granularity. The stacking of encoders captures the latent hierarchical structure of the check-in sequence, which is used to predict the next visiting POI. Extensive experiments on three public datasets demonstrate that the proposed model achieves superior performance whilst providing explanations for recommendations. Codes are available at https://github.com/JennyXieJiayi/STAR-HiT.

翻译：下一个利益点建议是基于位置的社会网络中的一项关键任务,但由于用户运动中表现出的高度差异和个人化,因此仍然具有挑战性。在这项工作中,我们探索由用户检查序列中多色性短期结构模式构成的潜在等级结构。我们为下一个基于位置的社会网络建议建议使用堆叠的等级编码器来重新编码时空环境环境,并明确定位不同颗粒的子序列。更具体地说,在每一个编码中,全球注意层可以捕捉该序列的多色度短期结构结构。我们为每个子序列中执行的本地注意层能够利用本地环境进行后序模型。序列分区层的推论位置和从全球背景的子序列序列的长度,这样子序列的顺序结构可以保存不同颗粒颗粒的颗粒颗粒颗粒。最后,在每一个编码中,全球注意层的下层结构将显示下层结构结构的尾部结构,从而显示每个尾部结构的尾部结构的尾部结构结构结构,从而显示新的尾部结构结构结构的尾部。