Booking.com employs sophisticated ranking to optimize search results for each user. The system uses advanced machine learning algorithms and leverages

The Engineering Behind High-Performance Ranking Platform: A System Overview

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2024-09-04 10:30:03

Booking.com employs sophisticated ranking to optimize search results for each user. The system uses advanced machine learning algorithms and leverages extensive data, including user behavior, preferences, and past interactions, to tailor hotel listings and travel recommendations.

In this article, we will take a peek into the architecture of the Ranking platform that underpins personalized ranking across various verticals (Accommodations, Flights, etc.)

The diagram below gives an overview of where the Ranking platform sits within the broader ecosystem. For simplicity, multiple systems have been condensed into a single block or omitted entirely to highlight the role of the Ranking platform.

A typical search flow unfolds as follows: the user initiates a call from their device or browser, and it traverses through various front-end systems, including micro-frontends and gateways, before reaching the search orchestrator. The core search engine then takes charge, orchestrating the search process and generating a list of properties for the search results page and maps. This task involves interfacing with an Availability Search Engine, which tracks the availability of tens of millions of properties across Booking.com over time. Given the extensive nature of this data, the Availability Search Engine is sharded to manage heavy queries efficiently. A coordinator distributes workload across shards and aggregates results within the availability system.

The ranking platform is behind the Availability Search Engine. It uses ML models to score the retrieved properties that match the search criteria.