Deep Neural Model for Point-of-Interest Recommendation Fused with Graph Embedding Representation

With the rapid popularity of smart mobile devices and the rapid development of location-based social networks (LBSNs), location-based recommendation has become an important method to help people find the attractive point-of-interest (POI). However, due to the sparsity of user-POI check-in data, the traditional recommendation model based on collaborative filtering cannot be well applied to the POI recommendation problem. In addition, location-based social networks are different from other recommendation scenarios, and users' POI check-ins are closely related to social relations and geographical factors. Therefore, this paper proposes a neural networks POI recommendation model fused with social and geographical graph embedding representation(SG-NeuRec). Our model organically combines social and geographical graph embedding representations with user-POI interaction representation, and captures the latent interactions between users and POIs under the neural networks framework. Meanwhile, in order to improve the accuracy of POI recommendation, the relevance between users' accessing time pattern and POI is modeled by the designed shallow network and unified under the same framework. Extensive experiments on two real location-based social networks datasets demonstrate the effectiveness of the proposed model.