Mobility-Embedded POIs: give LLMs a living sense of place
LLMs treat places as frozen words, but Google Research's ME-POIs framework layers in anonymized mobility patterns — arrival times, stay durations, daily foot traffic — to teach models a place's functional rhythm, not just its metadata. The payoff is concrete: up to 81.9% relative gain predicting visit intent, 75.1% for price level, and 24.7% for busyness, across unseen places. A self-supervised approach that makes embeddings encode when a place is alive, not merely what it is.