  Next-location prediction, consisting of forecasting a user's location given
their historical trajectories, has important implications in several fields,
such as urban planning, geo-marketing, and disease spreading. Several
predictors have been proposed in the last few years to address it, including
last-generation ones based on deep learning. This paper tests the
generalization capability of these predictors on public mobility datasets,
stratifying the datasets by whether the trajectories in the test set also
appear fully or partially in the training set. We consistently discover a
severe problem of trajectory overlapping in all analyzed datasets, highlighting
that predictors memorize trajectories while having limited generalization
capacities. We thus propose a methodology to rerank the outputs of the
next-location predictors based on spatial mobility patterns. With these
techniques, we significantly improve the predictors' generalization capability,
with a relative improvement on the accuracy up to 96.15% on the trajectories
that cannot be memorized (i.e., low overlap with the training set).
