Recommendation of Points-of-Interest Using Graph Embeddings

Abstract

The rapid growth of Location-based Social Networks (LBSNs) has lead to the generation of massive datasets which are collected in an exponential rate. The collected information may be used to facilitate users' needs with recommendations related to their past preferences. Many recommendation models were introduced in the literature, which learn by the history of users and provide recommendations for Points-of-Interest. Unfortunately, most of them ignore the relation existing among the temporal properties, the spatial attributes and the periodicity of the check-ins. In this work, we present a novel methodology, named JLGE, that combines all aforementioned factors into one unified approach which facilitates POI recommendations. In particular, the model jointly learns the embeddings of six informational graphs i.e., two unipartite (user-user and POIPOI) and four bipartite (user-location, user-time, location-user, and location-time) into the same latent space and personalize the recommendations based on these embeddings. We have experimentally evaluated the accuracy of our model using two real-world datasets in terms of the top-n POIs recommendations. The performance evaluation results indicate a significant improvement in accuracy, in comparison to another state-of-theart graph-based approach.