论文标题
STP-udgat：下一个POI推荐的空间 - 时间段用户用户尺寸图表网络
STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation
论文作者
论文摘要
下一个利益点（POI）建议是基于位置的社交网络（LBSN）和运输的整个领域的一个长期问题。基于独立的用户访问序列，基于重复的神经网络（RNN）方法在本地视图中学习POI-POI的关系。这限制了该模型在全球视图中直接连接和学习的能力，以推荐经过语义训练的POI。在这项工作中，我们提出了一个空间 - 乘数用户尺寸图形注意网络（STP-udgat），这是一个新颖的探索探索模型，该模型同时利用个性化的用户偏好，并探索全球空间 - 暂时性 - 暂时性 - 偏好（STP）社区中的新POI，同时允许用户从其他用户中选择性地学习。此外，我们提出随机步行作为掩盖的自我发项选项，以利用STP图的结构，并在探索过程中找到新的高阶POI邻居。六个现实世界数据集的实验结果表明，我们的模型大大优于基线和最先进的方法。
Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation. Recent Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in a local view based on independent user visit sequences. This limits the model's ability to directly connect and learn across users in a global view to recommend semantically trained POIs. In this work, we propose a Spatial-Temporal-Preference User Dimensional Graph Attention Network (STP-UDGAT), a novel explore-exploit model that concurrently exploits personalized user preferences and explores new POIs in global spatial-temporal-preference (STP) neighbourhoods, while allowing users to selectively learn from other users. In addition, we propose random walks as a masked self-attention option to leverage the STP graphs' structures and find new higher-order POI neighbours during exploration. Experimental results on six real-world datasets show that our model significantly outperforms baseline and state-of-the-art methods.
