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Wayfair Data Science Launches New Video Explainer Series!

Wayfair Data Science Launches New Video Explainer Series!

Rachel Kirkwood Rachel Kirkwood January 7th, 2019

Find out about how Wayfair tackles product recommendations in our first installment!   Wayfair Data Science is composed of a number of sub-teams, each tackling a different set of specific business challenges. In order to give you a taste of the wide array of people and workstreams we have here……

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Information Retrieval and Machine Learning at Wayfair

Information Retrieval and Machine Learning at Wayfair

Rachel Kirkwood Rachel Kirkwood July 6th, 2018

Come find us! Next week, Wayfair Data Science will be hitting the road. Data scientists and engineers from across our teams will be attending the SIGIR Conference on Research and Development in Information Retrieval in Ann Arbor, Michigan, as well as the International Conference on Machine Learning (ICML) in Stockholm, Sweden. Here……

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Everywhere You Look: Computer Vision at Wayfair

Everywhere You Look: Computer Vision at Wayfair

Patricia Stichnoth Patricia Stichnoth June 8th, 2018

Next week, Wayfair Data Science will be at the Computer Vision and Pattern Recognition conference in Salt Lake City. We’re bringing data scientists and engineers from several different teams, plus a VR rig to try out the shopping experience of the future (come find us at booth 224!). We pride……

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Object Detection and Visual Search Improvements

Object Detection and Visual Search Improvements

Cung Tran Cung Tran May 18th, 2018

Last year, Wayfair launched Visual Search, a new and novel way to find products on our website. Users can now upload photos of furniture they like and find visually similar matches in an instant. If you need a little refresher on this technology, the original blog post can be found……

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A Cornucopia of Area Rugs: Will a Diverse Set of Choices Help Customers Find More of What They Love?

A Cornucopia of Area Rugs: Will a Diverse Set of Choices Help Customers Find More of What They Love?

Daniel Saunders Daniel Saunders February 20th, 2018

Right now, Wayfair has about 86,000 rugs available for purchase. On the first page of our rug category page, however, we only have room for the top 48. You see even fewer if you don’t happen to scroll or swipe down. When it comes to our customer’s experience, what we……

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Recommending Visually Similar Products Using Content Based Features

Recommending Visually Similar Products Using Content Based Features

Vinny DeGenova Vinny DeGenova December 1st, 2017

Multiple recommendation systems at Wayfair use collaborative filtering based models to understand user behavior and identify like-minded customers. Despite its success, collaborative filtering has a few significant drawbacks, such as the cold start problem and a limited scope of recommendable products. By leveraging image based product embeddings, Wayfair has created……

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Better Lucene/Solr searches with a boost from an external naive Bayes classifier

Better Lucene/Solr searches with a boost from an external naive Bayes classifier

Ben Clark Ben Clark October 23rd, 2012

Me: Doug, what are you doing? Doug: Solving the problem of class struggle with one of Greg’s classifiers. Me:  Karl Marx should call his office.  What do you mean by that? Doug: Let me explain… Class struggle at Wayfair search used to manifest itself as searches for ‘red cups’ that returned……

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Information Week Interviews Wayfair on its use of Markov Clustering

Information Week Interviews Wayfair on its use of Markov Clustering

Avatar Wayfair September 28th, 2012

These days, in the big data community, we often hear how biologists have adopted and are using distributed computing technologies that were first introduced to solve problems in software engineering. The fact that Wayfair has done the inverse and used a tool initially developed to help biologists cluster similar proteins……

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Recommendations with Markov Clustering

Recommendations with Markov Clustering

Ben Clark Ben Clark February 23rd, 2012

Our story begins in Holland in 1997, where a researcher named Stijn van Dongen, who is pretty good at Go, has a 5-minute flash of insight into modeling flows with stochastic matrices.  He writes a thesis about it and makes a toolkit called MCL with a free software license. Flash……

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Recommendations with simple correlation metrics on implied preference data

Recommendations with simple correlation metrics on implied preference data

Ben Clark Ben Clark January 30th, 2012

When you sit down to write a recommendations system, there are quite a few  well-practiced techniques you can use, and it’s difficult to know in advance how well they are going to work out when applied to your data.  Thanks to the Netflix prize, which was initiated in 2006……

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