# Unlocking the Power of Customization: How Our Enrichment System Transforms Recommendation Data...

DevFeed: [Unlocking the Power of Customization: How Our Enrichment System Transforms Recommendation Data...](<https://devfeed.tech/articles/unlocking-the-power-of-customization-how-our-enrichment-system-transforms-recommendation-data-23727.md>)

Original publisher: [Read original article](<https://medium.com/booking-com-development/unlocking-the-power-of-customization-how-our-enrichment-system-transforms-recommendation-data-e71832fc4ef4?source=rss----1c36c35f9c76---4>)

Author: Juan Pablo Lorenzo

Published: 2025-06-18T08:02:41Z

Content type: article

Language: en

Sources: [Booking.com Development - Medium](<https://devfeed.tech/sources/booking-com-development-medium.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [implementation](<https://devfeed.tech/topics/implementation.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [article](<https://devfeed.tech/tags/article.md>), [data](<https://devfeed.tech/tags/data.md>), [java](<https://devfeed.tech/tags/java.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [software-development](<https://devfeed.tech/tags/software-development.md>)

## AI overview

This Booking.com engineering article describes an Enrichment System for its Recommendation Platform. The system is intended to provide reusable contextual data about recommendations, while addressing the complexity and tight coupling of the previous enrichment process.

## Source excerpt

Unlocking the Power of Customization: How Our Enrichment System Transforms Recommendation Data Enrichments How are accurate property prices on Booking.com connected to machine learning that recommends appealing property photos? What about the number of users who have wishlisted a property? And how can developers assess if their recommendation models effectively boost traveler clicks? None of these pieces of information are recommendations on their own, but they're crucial when providing our travelers good recommendations. For years, our Recommendation Platform has handled this process, but we needed a better way. Recommendation Platform is a service created to empower teams with adaptive, scalable, and personalized recommendations. These recommendations are integrated into every step of the customer journey, covering everything from attractions and flights to travel destinations and accommodation. We allow teams to use machine learning models combining multiple providers to achieve traveler recommendations. The platform is developed to be self-served and new use cases could be implemented by those teams. Check out our Self-Serve Platform for Scalable ML Recommendations article for a deeper look into how the platform works. Recommendations are valuable by themselves, but in most cases, information about the specific recommendation is just as important. Previously, adding such information was complex, lacked isolation, and had limited reusability. The new Enrichment System aims to overcome these challenges. The Enrichment System is a unified, extensible and scalable approach to support enrichments in the Recommendation Platform. When using data to develop new capabilities, being fast and efficient is key. But our previous enrichment process often stood as a barrier rather than a facilitator. Here's why: Complexity of implementation: imagine a developer eager to enhance a recommendation with new data insights. Under the old system, implementing a new enrichment require