Wayfair paper accepted at RecSys 2022 applies state-of-the-art method to generate product recommendations
Style transfer: How Wayfair leverages customer browse activity in one furniture category to inform recommendations for a category they have never shopped before.
2 Min Read
Building Wayfair’s First Virtual Assistant: Automating Customer Service by Text Based Intent Prediction
Not everyone wants to speak with someone when they need to set up a return or report an issue with their order. We built an NLP-powered Virtual Assistant to provide our customers a fast and easy option that’s available 24/7 to resolve their service-related issues.
10 Min Read
Wayfair and its subsidiary brands operate globally and we send out millions of marketing emails every day to communicate with our customers. To improve customer satisfaction and increase customer engagement, we have developed a new generation of daily sales email models (Nightingale) to make email sending decisions. The Nightingale model is built via a scalable retraining pipeline to serve different business needs across all regions and brands.
10 Min Read
Today, many companies rely heavily on third-party event tracking platforms for collecting event data. Perhaps the most well-known of these is Google Analytics. However, at Wayfair, we chose to go another route, largely eschewing the third-party vendor model to develop our own first-party event tracking platform. It’s called Scribe.
5 Min Read
I recently had the honor of speaking at EmTech Digital, MIT Technology Review’s signature conference on artificial intelligence (AI) and business leadership. This isn’t my first time speaking at the conference, but it is my first time doing it in person.
3 Min Read
As a technology company, Wayfair encourages all team members to roll up their sleeves and innovate. We take immense pride in how our work impacts customers and contributes to the business’s success. We also derive great satisfaction when a team member is recognized by the larger technology community.
2 Min Read
Machine learning (ML) profoundly transformed the digital shopping experience for millions of people worldwide. We’ve seen this at Wayfair, where our teams are continuously delivering intelligent and improved experiences for our customers by integrating ML at every stage of their journey with us. Right now Wayfair is leveraging hundreds of ML applications, which on the outside are improving marketing campaigns, improving the visual merchandising of the products we sell, personalizing product recommendations, enabling high-quality customer service, and more.
6 Min Read
When customers submit search queries, such as “ivory jute oriental,” it is essential for the search engine to understand which types of products they are looking for. In this case, the customer is likely shopping for an area rug. Equipped with this knowledge, we can tailor the search results page by displaying area rug-specific filters and banners and refine the search results by first showing area rugs. At Wayfair Search, the Query Classifier Model performs this task of real-time prediction of the desired product types based on user search queries. This blog post discusses how we designed, trained, and deployed this deep learning classification model at SearchTech.
8 Min Read
Customers’ tastes may change over a period of time. How can we leverage their browse history to make sure our recommendations reflect their most up-to-date preferences?
7 Min Read
Some experiments at Wayfair can last 60 days or more. To speed up learning in experiments while still optimizing for long term rewards, our team developed a data science platform called Demeter, that uses ML models to forecast longer term KPIs based on customer activity in the short term. In this post we provide an overview for Demeter and its theoretical foundation in causal inference
11 Min Read