# Highlights of Booking.com's publication in 2025

DevFeed: [Highlights of Booking.com's publication in 2025](<https://devfeed.tech/articles/highlights-of-booking-com-s-publication-in-2025-30451.md>)

Original publisher: [Read original article](<https://booking.ai/highlights-of-booking-coms-publication-in-2025-1c1a6deba066?source=rss----4d265f07defc---4>)

Author: Yang Yang

Published: 2026-01-20T09:20:13Z

Content type: article

Language: en

Sources: [Booking.com Data Science](<https://devfeed.tech/sources/booking-com-data-science.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [2025](<https://devfeed.tech/tags/2025.md>), [ai](<https://devfeed.tech/tags/ai.md>), [booking](<https://devfeed.tech/tags/booking.md>), [compression](<https://devfeed.tech/tags/compression.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [latency](<https://devfeed.tech/tags/latency.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [publication](<https://devfeed.tech/tags/publication.md>)

## AI overview

Booking.com highlights its 2025 machine learning publications, including papers accepted at major conferences and research on applying Medusa speculative decoding and knowledge distillation to travel-related language model tasks.

## Source excerpt

At Booking.com, our mission is to make experiencing the world easier for everyone. We are committed to investing in cutting-edge technology that removes the barriers to travel, enabling seamless connections between millions of travelers and unforgettable experiences, diverse transportation options, and exceptional accommodations. The intersection of academic rigor and industry application is where true transformation happens. In 2025, our ML community bridged this gap more effectively than ever, contributing vital new insights to the global scientific community. With 8 out of 13 papers accepted at premier conferences -- including NeurIPS, SIGIR, KDD, and ACL -- our colleagues have demonstrated world-class expertise in AI, NLP, recommendation systems, uplift modeling, etc. These aren't just theoretical wins; they are the engines of innovation that allow us to push technological boundaries, ensuring our platform remains the most sophisticated and intuitive guide in the ever-evolving travel industry. Below, we highlight some of the key achievements and insights from these groundbreaking works. Speed Without Sacrifice: Fine-Tuning Language Models with Medusa and Knowledge Distillation in Travel Applications By Daniel Zagyva, Emmanouil Stergiadis, Laurens Van Der Maas, Aleksandra Dokic, Eran Fainman, Ilya Gusev, Moran Beladev Best paper award of 2025 ACL Industry Track https://aclanthology.org/2025.acl-industry.48/ In high-stakes industrial NLP applications, balancing generation quality with speed and efficiency presents significant challenges. We address them by investigating two complementary optimization approaches: Medusa for speculative decoding and knowledge distillation (KD) for model compression. We demonstrate the practical application of these techniques in real-world travel domain tasks, including trip planning, smart filters, and generating accommodation descriptions. We introduce modifications to the Medusa implementation, starting with base pre-trained models