Machine Learning & Artificial Intelligence

In July we launched Decorify, our generative AI model that creates shoppable, photorealistic images to enable consumers to envision their own homes in new styles by simply uploading a picture of their space. The feedback received has been great with users uploading more than 10,000 images and conducting just over 70,000 designs.

Note: This article originally appeared here on the Google Cloud blog.

Note: This article originally appeared here on the Google Cloud blog.

This article is co-authored by Trevor LaViale, Machine Learning Solutions Engineer at Arize AI

Wayfair is a company that believes growth is built on a strong global presence. Localization is a key element in successfully establishing ourselves in many markets and achieving the desired growth.

I recently had the pleasure of speaking at Transform 2023 in San Francisco, an event dedicated solely to an innovation that has taken the industry by storm, Generative AI. It was a great event that attracted speakers from a variety of iconic brands, including Google, Johnson & Johnson, Walmart, Fidelity Investments, eBay, FedEx Dataworks, McDonald's Corp, Intel, and the list goes on.

One of our missions at Wayfair is to help our 22 million customers find the products they are looking for. For example, when a customer searches for a “modern yellow sofa” on Wayfair, we want to show the most relevant options from the tens of thousands of sofas available in our catalog. To do that, it is important to have a strong understanding of the products we sell. We use machine learning algorithms to analyze and understand the descriptive information (e.g. color, shape) of over 40 million products provided by more than 20 thousand suppliers in our catalog.

How Wayfair leverages Graph Neural Networks to thwart account-hopping fraudsters

Geo experimentation unlocks incrementality testing when customer-level experimentation is not possible.

How to make deliveries faster without touching any packages

Let’s look at how we at Wayfair use custom embedding models to capture complex customer behavior patterns for advanced Fraud prevention.

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.