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A considerable amount of mobility data has been accumulated due to theproliferation of location-based service. Nevertheless, compared with mobilitydata from transportation systems like the GPS module in taxis, this kind ofdata is commonly sparse in terms of individual trajectories in the sense thatusers do not access mobile services and contribute their data all the time.Consequently, the sparsity inevitably weakens the practical value of the dataeven it has a high user penetration rate. To solve this problem, we propose anovel attentional neural network-based model, named AttnMove, to densifyindividual trajectories by recovering unobserved locations at a fine-grainedspatial-temporal resolution. To tackle the challenges posed by sparsity, wedesign various intra- and inter- trajectory attention mechanisms to bettermodel the mobility regularity of users and fully exploit the periodical patternfrom long-term history. We evaluate our model on two real-world datasets, andextensive results demonstrate the performance gain compared with thestate-of-the-art methods. This also shows that, by providing high-qualitymobility data, our model can benefit a variety of mobility-oriented down-streamapplications.

AttnMove: History Enhanced Trajectory Recovery via Attentional Network