Smart Matching Platform

Luxury travel clients expect highly personalized recommendations, but matching them to the right hotels and experiences requires analyzing many different factors. The challenge was to design a system that could combine open data with client preferences and hotel attributes to deliver tailored suggestions at scale.

The system was built to use machine learning and vector representations to evaluate hotels across ~40 scoring parameters. By blending internal client preferences with external data, it produced top‑N recommendations that aligned with each client’s expectations for luxury experiences.

• – Matches clients to hotels and experiences using ~40 scoring parameters
• – Combines open data with internal client preferences
• – Outputs top-N personalized recommendations

• – Classical ML
• – Vector representations
• – Scoring & similarity matching

The prototype provided a structured way to personalize luxury travel recommendations. It helped demonstrate how machine learning can balance multiple parameters and client preferences, giving travel providers a more data‑driven approach to matching clients with the experiences that fit them best.