  The rapid growth of users' involvement in Location-Based Social Networks
(LBSNs) has led to the expeditious growth of the data on a global scale. The
need of accessing and retrieving relevant information close to users'
preferences is an open problem which continuously raises new challenges for
recommendation systems. The exploitation of Points-of-Interest (POIs)
recommendation by existing models is inadequate due to the sparsity and the
cold start problems. To overcome these problems many models were proposed in
the literature, but most of them ignore important factors such as: geographical
proximity, social influence, or temporal and preference dynamics, which tackle
their accuracy while personalize their recommendations. In this work, we
investigate these problems and present a unified model that jointly learns
users and POI dynamics. Our proposal is termed RELINE (REcommendations with
muLtIple Network Embeddings). More specifically, RELINE captures: i) the
social, ii) the geographical, iii) the temporal influence, and iv) the users'
preference dynamics, by embedding eight relational graphs into one shared
latent space. We have evaluated our approach against state-of-the-art methods
with three large real-world datasets in terms of accuracy. Additionally, we
have examined the effectiveness of our approach against the cold-start problem.
Performance evaluation results demonstrate that significant performance
improvement is achieved in comparison to existing state-of-the-art methods.
