Abstract
The goal of location representation learning is to learn an embedded feature vector for each location. We propose a Time-Aware Location Embedding (TALE) method to learn distributed representations of locations from users’ spatio-temporal trajectories, in which a novel tree structure is designed to incorporate the temporal information in the hierarchical softmax model. We utilize TALE to improve two location-based prediction tasks to verify its effectiveness.
This work is supported by the Natural Science Foundation of China (No. 61603028).
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Wan, H., Li, F., Guo, S., Cao, Z., Lin, Y. (2019). Learning Time-Aware Distributed Representations of Locations from Spatio-Temporal Trajectories. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_26
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DOI: https://doi.org/10.1007/978-3-030-18590-9_26
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