Hurb builds travel destination similarity recommendation system using Flyte and BERTimbau
Travelers find it hard to choose their next destination, and collaborative filtering — the standard recommendation approach — was not viable because the team had no access to internal data, travel is highly seasonal making pattern extraction impractical, and the goal was to recommend atypical places Hurb does not yet serve.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User city query
A user specifies a city and state and how many recommendations they want.
The team built a working end-to-end destination recommendation prototype covering about 440 Brazilian cities, orchestrated in Flyte and exposed via a Streamlit web interface, winning the MLOps Community hackathon.
What failed first
Collaborative filtering was explicitly ruled out: no internal data was available for the hackathon, seasonal travel patterns made sufficient data collection impractical, and the goal required recommending destinations outside Hurb's existing inventory.