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There are 90 results that match your search. 90 results
Left image shows population of millions of users being split into test groups A and B. Right images shows 50 states comprised of 210 geos being split int tests groups A and B
Wayfair Tech Blog
How Wayfair Uses Geo Experiments to Measure Incrementality
Wiley Jennings
By  Wiley Jennings
, 
April 26, 2023
Geo experimentation unlocks incrementality testing when customer-level experimentation is not possible.
Pillow delivery time
Wayfair Tech Blog
Delivery-Date Prediction
Matthias Mozer
By  Matthias Mozer
, 
April 13, 2023
How to make deliveries faster without touching any packages
a flow diagram shows the transition between multiple wayfair webpages being represented by a vector of integers in an embedding space
Wayfair Tech Blog
Introducing Melange: A Customer Journey Embedding System for Improving Fraud and Policy Abuse Detection
By  Viacheslav Dubrov
, 
April 4, 2023
Let’s look at how we at Wayfair use custom embedding models to capture complex customer behavior patterns for advanced Fraud prevention.
ndcg_with_beta_final.png
Wayfair Tech Blog
Wayfair releases WANDS, the largest and richest publicly available dataset for e-commerce product search relevance
Weiyi Sun
By  Weiyi Sun
, 
February 28, 2023
Search relevance – the relationship between users’ queries and the products returned in search results – is one of the most important performance indicators for ecommerce storefronts. However, the sheer volume of the data makes evaluating and improving search relevance a difficult proposition. To give just one example, the Wayfair catalog has millions of products, which makes it difficult to create relevance-focused evaluation datasets manually.
CI/CD and Scheduling pipeline for Vertex AI
Wayfair Tech Blog
How Wayfair is reaching MLOps excellence with Vertex AI
Christian Rehm
By  Christian Rehm
, 
February 14, 2023
Dive into the ways Wayfair’s data scientists are using Vertex AI to improve model productionization, serving, and operational readiness velocity.
customup1
Wayfair Tech Blog
How Wayfair uses “Predicted Winners” Models to Accelerate Success for New Products
Shuo Zhao
By  Shuo Zhao
, 
February 7, 2023
Wayfair works with over 25,000 suppliers to sell more than 30 million products in a variety of styles from classic to contemporary at compelling price points. In order to create a frictionless shopping experience, we need to enable customers to explore our massive catalog efficiently, and find the right products that help them create their unique sense of home.
a flow diagram shows how we use first heuristics, then a neural network to separate positive labels from negative labels
Wayfair Tech Blog
Hamlet: Wayfair's ML Approach to Identifying Business Shoppers
Sanjana Kacholia
By  Sanjana Kacholia
, 
January 19, 2023
To B2B, or not B2B, that is the question.
A screenshot of the Wayfair Search Page displaying a search for "hunter green table". Search results show rugs, chair cushions, and tables
Wayfair Tech Blog
How Wayfair’s Scientists Collaborated with Innovative Startup ThirdAI to Serve Hyper-Relevant Search Results to Customers
Weiyi Sun
By  Weiyi Sun
, 
January 4, 2023
Wayfair has tens of millions of products that serve the needs of over 33 million customers. Each of our customers have nuanced preferences as they buy products to help them create their own unique sense of home. Our search and recommender systems play an important role in helping customers find these products as quickly and conveniently as possible.
ColinGray.jpg
Wayfair Tech Blog
A Senior Economist at Wayfair Provides Three Tips for PhD Students to Land a Highly Selective Internship at the Company
Wayfair
By  Wayfair
, 
December 20, 2022
According to Colin Gray, a growing number of economists are being drawn to the prospect of carrying out interesting work at technology companies like Wayfair.