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Ingrid Xu

August 14, 2019
Wayfair sells over 10 million products on our website. This vast selection ensures that customers have numerous options when shopping for a particular item; but it also makes effective, personalized product recommendations of vital importance in helping our customers find products that are relevant to their interests. This week in Wayfair Data Science’s explainer series, Data Scientist Samuel Yusuf discusses the two main domains of collaborative filtering (memory based and model based) and how they can be applied to make predictions on a customer’s preference for a product.
1 Min Read
February 19, 2019
This week, Zephy McKanna sets out to explain Multi-Armed bandits. Here at Wayfair, we are constantly testing new algorithms, product sort orders, marketing, and other messages using epsilon-first Multi-Armed Bandits – also known as A/B tests. We are also employing some more interesting MAB algorithms, like Thompson Sampling, to dynamically balance traffic during tests on low-traffic portions of the site (like holiday décor), as well as for some long-standing dynamic problems like choosing the top N among the ever-changing sales events to send out in emails to default customers.
1 Min Read
February 4, 2019
This week in Wayfair Data Science’s explainer series, Tim O’Connor discusses experimentation in the context of data science. Experiments are crucial to data science, helping to determine which version of a model is to use in future iterations of a system or generating new sources of data unavailable in a company’s historical data. As such, designing and conducting experiments is a core capability for data scientists at Wayfair. In this video, Tim explains the basics behind A/B tests and synthetic controls, and discusses how they are used to tackle various questions at the company.
1 Min Read
January 21, 2019
Welcome back to Wayfair Data Science's Explainer Series! This week, Afshaan Mazagonwalla will be speaking about Bayesian Machine Learning
1 Min Read