Recommending yet-unvisited points of interest (POIs) which may be of interest to users is one of the fundamental applications in location-based social networks. It mainly replies on the understanding of users, POIs, and their interactions. Previous studies either develop matrix factorization-based approaches or utilize deep learning frameworks to learn better representation of users and POIs in order to estimate users’ latent preference. However, most of existing methods still confront the challenges like in traditional recommender systems, such as data sparsity and cold-start. In particular, they have difficulties in fully utilizing rich semantic information, such as social influence, geographical constraints and interactions between users and POIs. To fill this research gap, we propose a new recommendation framework – Hybrid Graph convolutional networks with Multi-head Attention for POI recommendation (HGMAP). HGMAP constructs a spatial graph based on the geographical distance between pairs of POIs and leverages Graph Convolutional Networks (GCNs) to express the high-order connectivity among POIs, which not only incorporates the spatial constraints but also provides an effective way to alleviate the sparse check-in problem. In addition, HGMAP exploits the user social relationship with another GCN and differentiates user preference over different aspects of POIs with a multi-head attention mechanism. We conducted extensive experiments on three public datasets and the results demonstrate that HGMAP significantly improves the recommendation performance over several state-of-the-art models, for example, up to approximately 4.8% and 7% for Precision@10 and Recall@10, respectively.

推荐用户可能感兴趣的尚未访问的兴趣点（POI）是基于位置的社交网络的基本应用之一。它主要依赖于对用户，POI及其交互的理解。先前的研究要么开发基于矩阵分解的方法，要么利用深度学习框架来更好地了解用户和POI，以估计用户的潜在偏好。但是，大多数现有方法仍面临像传统推荐系统中的挑战，例如数据稀疏性和冷启动。特别是，他们在充分利用丰富的语义信息方面遇到困难，例如社会影响力，地理限制以及用户和POI之间的交互。为了填补这一研究空白，我们提出了一个新的推荐框架-具有POI推荐的多头注意的混合图卷积网络（HGMAP）。HGMAP基于两对POI之间的地理距离构造空间图，并利用图卷积网络（GCN）表示POI之间的高阶连通性，这不仅合并了空间约束，而且为减轻稀疏检查提供了有效的方法-问题。另外，HGMAP利用多头关注机制利用用户与另一个GCN的社交关系，并区分用户对POI不同方面的偏好。我们在三个公开数据集上进行了广泛的实验，结果表明HGMAP可以显着改善几种最先进模型的推荐效果，例如，Precision @ 10和Recall @ 10。