Understanding and measuring the effect of human mobility on the spread of epidemics is key to addressing these threats. GPS human mobility data represents an enormous advancement in the field of epidemics as it could reveal population-level contact patterns that can replace homogeneous mixing assumptions or the unjustified use of synthetic random networks in epidemic models. However, a standing challenge in the estimation of contacts from GPS signals is addressing the high sparsity in this type of data. Alas, most users are observed only for a small fraction of the time. In this paper, we address this issues by proposing a novel methodology that can fill in the gaps in the data. Our framework is based on link prediction using deep learning to predict missing links in a temporal bipartite graph connecting users and locations. We demonstrate and validate our methodology on privacy-enhanced location data from thousands of mobile devices in the city of Philadelphia during 2020.