User foundation models for Grab

This post introduces Grab's development of a user foundation model, a transformer-based architecture that learns from both tabular and time-series user interaction data. It addresses the challenge of integrating diverse data modalities (text, numerical, IDs, locations) and scaling to millions of entities. The model generates user embeddings that generalize across various downstream tasks like recommendation, advertisement optimization, fraud detection, and churn prediction, moving away from manually engineered features. The architecture unifies order-invariant tabular data with order-dependent time-series data using a `key:value` tokenization strategy.