Using CaLLiPer, a contrastively trained location encoder that fuses coordinates and POI text, improves next-location prediction on some mobility datasets and shows the largest gains when test locations are unseen during training.
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Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Using CaLLiPer, a contrastively trained location encoder that fuses coordinates and POI text, improves next-location prediction on some mobility datasets and shows the largest gains when test locations are unseen during training.