Making Grab’s Everyday App Super

This post details the evolution of the Grab Feed recommendation engine, introducing strategies such as popularity, user favorites, collaborative filtering, habitual behavior, and deep recommendations that leverage cross-business data. It also discusses the use of an experimentation platform to measure recommendation quality through online and offline metrics, and outlines future work including multi-objective optimization, hyperparameter optimization automation, online learning, multi-armed bandits, and a recsplanation system.