Sparsity of the User-POI matrix is a well established problem for next POI recommendation, which hinders effective learning of user preferences. Focusing on a more granular extension of the problem, we propose a Joint Triplet Loss Learning (JTLL) module for the Next New ($N^2$) POI recommendation task, which is more challenging. Our JTLL module first computes additional training samples from the users' historical POI visit sequence, then, a designed triplet loss function is proposed to decrease and increase distances of POI and user embeddings based on their respective relations. Next, the JTLL module is jointly trained with recent approaches to additionally learn unvisited relations for the recommendation task. Experiments conducted on two known real-world LBSN datasets show that our joint training module was able to improve the performances of recent existing works.

翻译：用户- POI 矩阵的分化是下一个 POI 推荐的既定问题,它阻碍有效学习用户偏好。我们着眼于问题更细的延伸,为下一个新的 POI 推荐任务提议了一个联合三联损失学习模块(JTLL),这更具挑战性。我们的 JTLL 模块首先从用户历史 POI 访问序列中计算额外的培训样本,然后,提出一个设计好的三联损失功能,以减少和增加 POI 和用户嵌入的距离,这取决于他们各自的关系。接下来, JTLL 模块与最近的方法共同培训,以学习建议任务未访问的关系。对两个已知真实世界 LBSN 数据集进行的实验表明,我们的联合培训模块能够改进最近工程的绩效。