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Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations by Park, Chung; Kim, Taesan; Hong, Junui; Choi, Minsung; Choo, Jaegul is a scholarly article available to read on EtoBox.

What is Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations about?

Pre-training the embedding of a location generated from human mobility data has become a popular method for location based services. In practice, modeling the location embedding is too expensive, due to the large number of locations to be trained in situations with fine-grained resolution or extensive target regions. Previous studies have handled less than ten thousand distinct locations, which is insufficient in the real-world applications. To tackle this problem, we propose a Geo-Tokenizer, designed to efficiently reduce the number of locations to be trained by representing a location as a combination of several grids at different scales. In the Geo-Tokenizer, a grid at a larger scale shares the common set of grids at smaller scales, which is a key factor in reducing the size of the location vocabulary. The sequences of locations preprocessed with the Geo-Tokenizer are utilized by a causal location embedding model to capture the temporal dependencies of locations. This model dynamically calculates the embedding vector of a target location, which varies depending on its trajectory. In addition, to efficiently pre-train the location embedding model, we propose the Hierarchical Auto

Author

Park, Chung; Kim, Taesan; Hong, Junui; Choi, Minsung; Choo, Jaegul

Published

2023

Language

EN