Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations
This work addresses a scalability bottleneck in location-based services for real-world applications with fine-grained or extensive regions.
The paper tackles the problem of expensive location embedding pre-training due to large numbers of locations by proposing a Geo-Tokenizer that reduces vocabulary size through hierarchical grid representations, and it shows significant performance improvements in downstream tasks with fewer parameters compared to existing methods.
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-regressive Location Model objective to effectively train decomposed locations in the Geo-Tokenizer. We conducted experiments on two real-world user trajectory datasets using our pre-trained location model. The experimental results show that our model significantly improves the performance of downstream tasks with fewer model parameters compared to existing location embedding methods.