#Uber updated its Uber Eats Home Feed recommendation system using near…

InfoQ@techhub.social · 2026-05-28 17:10

#Uber updated its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model.

By moving from hand-crafted features to a transformer-based sequence model, the system reduces feature freshness latency from ~24 hours to seconds.

🔗 Learn more about the update and the architecture behind it on #InfoQ ⇨ https://bit.ly/4dCly1K

#SoftwareArchitecture #DistributedSystems #MachineLearning #MLOps

Uber Improves Restaurant Recommendations Using Real-Time Signals and Listwise Ranking

Uber updates its Uber Eats Home Feed recommendation system using near real-time user sequence features and a Generative Recommender model. The system evolves from hand-crafted features to transformer-based sequence modeling, reduces feature freshness from 24 hours to seconds, and shifts from pointwise scoring to listwise GenRec for improved contextual ranking and real-time personalization.

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