NGPR: A comprehensive personalized point-of-interest recommendation method based on heterogeneous graphs

Nowadays, many people like to share the places they visited in the Location-based Social Networks (LBSNs). A Point of Interest (POI) recommendation, as one of the location-based services, helps users find new locations they prefer to visit. Recently, researchers have proposed many methods to leverage user-generated content, such as check-ins, for POI recommendation. However, due to the sparsity of user check-in information, it is still very difficult to recommend appropriate and accurate locations to users. To address the problem, in this paper, we propose a novel POI recommendation method named NGPR. Firstly, we construct a heterogeneous LBSN graph of users, POIs, categories and time periods. based on check-in records. Subsequently, the Node2Vec technique is employed to establish the latent vectors of POIs and users. Finally, we integrate comprehensive factors including the category preference, geographical distance and POI popularity for POI recommendation. The NGPR method is applied to two real LBSN datasets for experimental analysis. The experimental results show that the precision@5 of our method achieves 18.82% and 19.19% higher than that of the second best method on two real LBSN datasets respectively.

Automated summary

This paper proposes a method for POI recommendation using user-generated content. By constructing a heterogeneous graph and employing Node2Vec technique, the method establishes latent vectors and integrates multiple factors for recommendation. Experimental results demonstrate that this method outperforms other approaches in terms of accuracy on two real datasets.