Sequential location recommendation plays a huge role in modern life, which can enhance user experience, bring more profit to businesses and assist in government administration. Although methods for location recommendation have evolved significantly thanks to the development of recommendation systems, there is still limited utilization of geographic information, along with the ongoing challenge of addressing data sparsity. In response, we introduce a Proximity-aware based region representation for Sequential Recommendation (PASR for short), built upon the Self-Attention Network architecture. We tackle the sparsity issue through a novel loss function employing importance sampling, which emphasizes informative negative samples during optimization. Moreover, PASR enhances the integration of geographic information by employing a self-attention-based geography encoder to the hierarchical grid and proximity grid at each GPS point. To further leverage geographic information, we utilize the proximity-aware negative samplers to enhance the quality of negative samples. We conducted evaluations using three real-world Location-Based Social Networking (LBSN) datasets, demonstrating that PASR surpasses state-of-the-art sequential location recommendation methods

翻译：序列位置推荐在现代生活中扮演着重要角色，它能够提升用户体验、为企业带来更多利润，并协助政府管理。尽管得益于推荐系统的发展，位置推荐方法已经取得了显著进步，但地理信息的利用仍然有限，同时数据稀疏性问题也持续存在。为此，我们提出了一种基于邻近感知区域表示的序列推荐方法（简称PASR），该方法基于自注意力网络架构构建。我们通过一种采用重要性采样的新型损失函数来解决稀疏性问题，该函数在优化过程中强调信息丰富的负样本。此外，PASR通过在每个GPS点使用基于自注意力的地理编码器处理层次化网格和邻近网格，增强了地理信息的整合。为了进一步利用地理信息，我们采用邻近感知负采样器提升负样本质量。我们使用三个真实世界的基于位置的社交网络（LBSN）数据集进行了评估，结果表明PASR超越了当前最先进的序列位置推荐方法。