Personalized Restaurant Recommendations at Scale combining Transformer with Gradient-Boosted Ranking
Wolt’s Universal Venue Ranker (UVR) is a large-scale, sequence-aware ranking model for personalized restaurant recommendations, deployed across more than 30 countries. UVR replaces three previously independent models—Neural Collaborative Filtering, a second-pass ranker, and a first-time-user model—by combining a transformer with a gradient-boosted decision tree for ranking.
The model follows a two-stage design. In the first stage, an encoder-style transformer learns a personalized user state representation from historical restaurant purchase sequences enriched with spatiotemporal signals such as time and location. In the second stage, a CatBoostRanker uses the transformer output as an input feature alongside additional user-, venue-, user–venue-, and delivery-specific features to score and rank candidate venues.
In this talk, we present the model and service architecture, the training and evaluation setup, and both offline and online results from a multi-country online A/B test, demonstrating significant improvements in global conversion rate and new venue trial rate. We also share practical lessons from deploying and operating a multi-stage ranking model under strict latency constraints at global scale.
