Wayfair Tech Blog

Modeling Uplift Directly: Uplift Decision Tree with KL Divergence and Euclidean Distance as Splitting Criteria

Introduction

Wayfair DS Explains It All: Anurag Syal on Projection Mapping in 3D

At Wayfair, we do everything we can to help our customers find exactly the products they need to furnish their homes in the style they envision. But creating all of the necessary elements to allow them to do that is not as easy as one might think. Right now, if an artist designs a new stylistic look for a home from scratch, it takes weeks before we actually get to introduce the products in the market to fit that look. Interestingly, it is not product manufacturing, but the creation of 3D models for these products that is the slowest and most expensive part of this process. There are a few key reasons for this; first of all, 3D models are required for product manufacturing, but 3D modeling software licenses and experience modelers are pricey and hard to come by. Secondly, to create a production quality 3D model (using a software like 3DS Max or Maya), 3D modelers need numerous 2D images of the product from various angles; given this requirement, you can imagine how costly a single rework or a minor change could be.

Wayfair Tech Blog

Wayfair DS Explains It All: Lin Jia on Measuring Price Effects

Understanding price effects is of high importance to any business, but usually it's not easy to measure. To do this, Wayfair’s algorithms team has been designing modeling and experimental approaches so that we can disentangle the intricate web of causality. In this video, Wayfair Senior Data Scientist Lin Jia explains a couple of ways that we can measure price effects.

Wayfair Tech Blog

Wayfair DS Explains It All: Ilkay Yildiz on Learning From Comparisons

Stylistic preference is an important factor when a home goods customer is deciding which product to buy, but it is very difficult to identify and define. Although designers have established different style categories, labeling a scene as adhering to a particular style is a highly subjective task. Furthermore, customers often cannot verbalize their style preferences, but can identify their preferences by looking at images. Thus, it is crucial to show products in a room context that are tailored to a customer's taste.

Wayfair Tech Blog

Wayfair DS Explains It All: Samuel Yusuf on Collaborative Filtering

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.

Wayfair Tech Blog

Wayfair DS Explains It All: Jerry Chen on A/B Test Measurement Validation

Experimentation plays a central role in understanding the business impact of data science strategies and solutions. A few weeks ago, Wayfair Data Science Manager Jerry Chen shared one way Wayfair has improved the experimentation process by building a unified test design and measurement platform (Gemini) for our marketing AB tests (read the blog post here!). In this video, Jerry will provide an introduction to running large scale Monte Carlo simulations to validate/optimize test design and measurement methodology using historical data.

Wayfair Tech Blog

Lynx: Identifying Wayfair Customers’ Functional Needs

Introduction

Wayfair Tech Blog

Wayfair DS Explains It All: Jinnie Chen on Product Tagging

Product tags reveals the detailed characteristics of a product which can be used to power PDP (product display page) creation, searching, filter creation and more.This week in Wayfair Data Science’s explainer series, Senior Data Scientist Jinnie Chen outlines some of the strategies we applied in Wayfair to perform automated product tagging.

Wayfair Tech Blog

Wayfair DS Explains It All: Cole Zuber on Evaluating Recommender Systems

Serving effective personalized product recommendations is critical to providing a pleasant shopping experience for customers at Wayfair. To do this, the Wayfair Data Science team builds state of the art recommender systems that leverage the customer’s previous browsing history to surface products that match their interests. This week in Wayfair Data Science’s explainer series, Senior Data Scientist Cole Zuber describes how we approach evaluating these recommender systems.