#machine-learning

How we knew COVID was over (and what our models had to unlearn)

When we retrain, when we rebuild, and when we leave a model alone. By: Harrison Katz A forecast that carries weight The Forecasting Data Science team at Airbnb produces many of the forecasts the rest of the company plans around: demand, bookings, cancellations, and a range of finer cuts by market and segment, refreshed continuously across thousands of markets. The targets differ, and the models differ, but they have one thing in common: Other teams build on top of them. This means a forecast that is casually wrong is not a clean miss, as it might be in an academic setting. That’s because a sma

The Airbnb Tech Blog - Medium · EN

The guest journey, updated in real time: extending Airbnb’s sequence recommender with Chronon

How two new Chronon capabilities, Push Mode and NRT Model Transform, allows us to provide more relevant search results instantly as a guest explores, rather than waiting for the next batch run. By : Pengyu Hou , Yuli Han , Daochen Zha , Haozhen Ding , Xin Liu , Sophie Wang , Pallavi Adusumilli , Sherry Li , Henry Saputra , Chun How Tan , Huiji Gao , Yan Zhang , Stephanie Moyerman , Yi Li , and Sanjeev Katariya A guest’s interaction with Airbnb doesn’t pause to wait for a nightly batch job. Someone might browse a dozen listings on a Tuesday afternoon, run a new search that evening, and expect t

The Airbnb Tech Blog - Medium · EN

Personalization without user identity

How Airbnb uses proximity signals to personalize without relying on individual user history. By : Wei Jiang , Bin Xu , Bharathi Thangamani , Weiwei Guo , Sundar Srinivasavaradhan , Tracy Yu , Huiji Gao , Swapnil Ghike , Michael Kinoti Great personalization starts with knowing your user. But what happens when the user is a stranger? A significant share of Airbnb users arrive without a login, without a recent search history, or without any prior booking — especially those landing from paid advertising or organic search. For these users, the ML models that power search ranking, destination recomm

The Airbnb Tech Blog - Medium · EN

CASTLE: training natural language search before the first real query

How we addressed the cold-start problem for Airbnb’s natural language search by jointly generating synthetic training data — queries and relevance labels — from de-identified booking and search signals. By : Wendy Ran Wei , Hao Li , Weiwei Guo , Xiaowei Liu , Xueyin Chen , Chun How Tan , Malay Haldar , Soumyadip Banerjee , Kedar Bellare , Huiji Gao , Sanjeev Katariya , and Stephanie Moyerman Introduction Natural language search lets guests express intent in their own words, such as “romantic cabin near skiing” or “pet-friendly beach house that sleeps eight,” rather than navigating filters and

The Airbnb Tech Blog - Medium · EN

Qwen 3.8 27B on my laptop can interpret model weights, too

Yesterday's hot paper was Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights by Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab. The paper (OK, the authors classify is as a "blog post", but it's paper-shaped) found that frontier models, in particular GPT-6 Astra, can both interpret and modify the raw weights of a machine learning model - literally a text description of the model containing a bunch of floating point numbers. I've been playing with one of the examples from the paper and I'm delighted to share that Qwen 3.8 27B running on my laptop

Simon Willison · EN

Xiaomi-OCR-0: A 0.8B AI Model for Document Parsing and OCR

Learn how Xiaomi-OCR-0 handles document parsing and OCR tasks, including its technical specifications, benchmark scores, use cases, and limitations.

Hacker Noon · EN

Integrating Netflix’s Foundation Model into Personalization applications

Authors: Divya Gadde , Ko-Jen Hsiao , Dhaval Patel and Moumita Bhattacharya The motivation for building the Netflix Foundation Model for personalization was laid out in this previous blog post. The Netflix homepage is powered by several specialized models and it requires significant time and resources to maintain and continue innovating on these individual models. Therefore, we decided to centralize member preference learning and have one powerful model learned from comprehensive user interaction histories and content data at a large scale and have this model distribute its learnings to other

Netflix Tech Blog · EN