随着基于位置的社交网络的普及，地点推荐作为推荐系统的重要分支，在解决信息过载、提升用户体验、增加平台收益等方面的作用愈加明显。现有的地点推荐算法大多基于矩阵分解，难以刻画用户和地点之间复杂的交互关系；此外，在基于位置的社交网络中，社交信息是建立用户画像的重要数据来源，如何融合社交信息辅助地点推荐成为亟待解决的问题。本文研究了基于深度神经网络的地点推荐解决方案，通过设计基于社交信息的新型采样方式和正则化损失函数，从两个角度融合社交信息。在两个真实世界数据集上的实验表明，本文提出的方案可以极大提升地点推荐的精准度。

With the development of location based social network, location recommendation, a typical recommender system, plays a more and more significant role in addressing data overloading, enhancing user engagement and improving platforms’ profit. Most existing researches on location recommendation are based on matrix factorization, which cannot capture the complicated relation between users and locations. In addition, in location based social network, social relation data is important for building user demographics, and therefore it becomes a major concern that how to combine social relation data to help improving recommendation quality. In this paper, a location recommendation approach based on deep learning is studied. By designing two novel designs, a social-aware sampler and a social-enhanced regularizer, the social information is integrated. Extensive experiments on two real-world datasets demonstrate that the proposed methods can significantly improve the recommendation accuracy compared with existing models.