Wayfair Tech Blog

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Digital Advertising and Causal Impact: Using Data Science to Power a Delightful Customer Experience

As data scientists, we face a variety of problem types. One of our critical challenges is identifying the proper methodological approach to solve each problem. By doing this, we avoid force-fitting the wrong tool to solve a problem, and we avoid having to reformulate a question to fit a specific method.

Wayfair Tech Blog

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

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 choose to place at the top makes a big difference! At present, we don’t explicitly showcase a diverse array of products. On browse pages for categories such as rugs, couches, or coffee tables, we instead combine personalized recommendations with the general popularity of products.

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Wayfair Tech Blog

AI disruption in e-commerce - Wayfair Data Science talk on PAPIs

Wayfair offers a zillion things home, with one of the world's largest online selections of furniture, home furnishings, décor and goods, including more than eight million products from over 10,000 suppliers. Wayfair's expanding Visual Search team is leveraging deep learning and our expansive catalog of images to create an exciting new way for shoppers to find the perfect product for their homes.

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Wayfair Tech Blog

Using Geographic Splitting & Optimization Techniques to Measure Marketing Performance

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Wayfair Tech Blog

Boston events during the week of 25 July 2016, on augmented/virtual reality, data science and PHP

There are three events in Boston this week where Wayfair engineers will be speaking.

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Wayfair Tech Blog

Better Lucene/Solr Searches with a Boost From an External Naive Bayes Classifier

Me: Doug, what are you doing?

Wayfair Tech Blog

Information Week Interviews Wayfair on its use of Markov Clustering

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 together to solve a problem in e-commerce, piqued the curiosity of Information Week magazine, who asked us for an interview about our February blog post on using Markov clustering for generating recommendations http://tech.wayfair.com/recommendations-with-markov-clustering/. Read the interview here https://www.informationweek.com/big-data/big-data-analytics/online-retailer-uses-dna-research-to-connect-with-customers/d/d-id/1106475

Wayfair Tech Blog

Recommendations with Markov Clustering

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.

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Wayfair Tech Blog

Recommendations with Simple Correlation Metrics on Implied Preference Data

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 and awarded in 2009, a lot has been written on recommender systems for the Netflix data set. If you happen to have a product catalogue similar to Netflix's (those movies from the 60s are still being viewed and rated), and your users happen to have scored it with a 5-point explicit ratings system, there are some awesome advanced techniques and frameworks that you can take for a spin. Does that sound like you? Show of hands? I didn't think so. Our data is certainly nothing like that.