Friend Recommendation in Location-based Social Networks via Deep Pairwise Learning

Generating friend recommendations in location-based social networks is a challenging task, as we have to learn how different contextual factors influence users' behavior to form social relationships. For example, the contextual information of users' check-in behavior at common locations and users' activities at close regions may impact users' relationships. In this paper we propose a deep pairwise learning model, namely FDPL. Our model first learns the low dimensional latent embeddings of users' social relationships by jointly factorizing them with the available contextual information based on a multi-view learning strategy. In addition, to account for the fact that the contextual information is non-linearly correlated with users' social relationships we design a deep pairwise learning architecture based on a Bayesian personalized ranking strategy. We learn the non-linear deep representations of the computed low dimensional latent embeddings by formulating the top-k friend recommendation task at location-based social networks as a ranking task in our deep pairwise learning strategy. Our experiments on three real world location-based social networks from Brightkite, Gowalla and Foursquare show that the proposed FDPL model significantly outperforms other state-of-the-art methods. Finally, we evaluate the impact of contextual information on our model and we experimentally show that it is a key factor to boost the friend recommendation accuracy at location-based social networks.