Hybrid graph convolutional networks with multi-head attention for location recommendation

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% forPrecision@10andRecall@10, respectively.