Google Research Introduces ME-POIs: A Mobility-Informed Framework that Adds “How a Place Is Used” to Text-Based POI Embeddings
A team from Google Research and USC has released Mobility-Embedded POIs (ME-POIs), a framework that folds aggregate human movement into text-based place embeddings. The premise is that language models describe what a place is, but not how it is used. Two coffee shops can share a category, an address block, and a text vector, while one runs commuter turnover and the other holds customers for ninety minutes. ME-POIs encodes each visit as a contextualized vector, then uses contrastive learning to
A framework called ME-POIs adds information about how places are actually used to text-based descriptions of locations. Language models can describe what a place is through text, but they cannot capture how it functions in practice, such as whether a coffee shop serves commuters or hosts long-stay customers. The framework encodes individual visits as vectors using location, arrival time, and departure time data, then uses machine learning to create a summary vector for each place of interest that reflects its typical usage patterns. Testing on mobility data from two cities showed that adding ME-POIs improved performance on tasks like predicting visit intent and busyness levels, and a version trained only on movement patterns outperformed text-based embeddings on price-level classification.
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