Many approaches to ranking a list of products or search results are based on assigning a score to each item and sorting in descending order—in other words, greedy sorting approaches. In e-commerce, predictive models place the product that the customer is most likely to be interested in at the top,……
Meet Michael What is your current role? I am currently a Senior Engineer on the Machine Learning Platforms team What does your team do? How does it fit into the world of Wayfair? Data science and machine learning are incredibly complex domains which require expertise in the fields of mathematics,…
1. Introduction In the last century, scholars across a variety of fields have explored the mathematics of decision-making, or learning to intervene within a system. A challenge that relies on decision-making is fundamentally different, and more complex, than a classification problem. For example, designing a machine to distinguish a photo…
Introduction Creating a recommendation system for home decoration poses unique challenges: each customer has his/her own taste and would like to maintain a cohesive personal style across his/her home. We at Wayfair know that it is hard to describe that taste in words and then to search through huge product……
Part I: How to build tools that you can hand off to data scientists [feature of Hoover tool] Summary Business moves fast and data science runways are long. What can we do to remove friction and iterate on data science models as quickly as possible? In this two-part blog……
Wayfair has a huge catalog with over 14 million items. Our site features a diverse array of products for people’s homes, with product categories ranging from “appliances” to “décor and pillows” to “outdoor storage sheds.” Some of these categories include hundreds of thousands of products; this broad offering ensures that……
Introduction The main objective of our recommender systems is to narrow down Wayfair’s vast catalogue to assist customers in finding exactly the products they want. Most recommendation algorithms leverage a customer’s browsing history, which includes various types of interactions with products, such as clicks, add-to-cart actions, and orders. The diversity……
Introduction Every day, Wayfair receives tens of thousands of messages from customers, telling us about their experiences with our products and services through the likes of product reviews, return comments, and various surveys. Considering that our customers spend valuable time writing these, it is fair to assume that they have……
Most machine learning algorithms are designed to train on balanced datasets. Resultantly, when our data are highly imbalanced, a typical model will have atrocious recall. In this video, Wayfair Senior Data Scientist Trent Woodbury explains the three most common ways of handling this imbalanced data problem. Trent Woodbury……
This week in Wayfair Data Science’s Explainer Series, Data Science Tech Lead Peter B. Golbus discusses machine learning from a theoretical computer science perspective. In this video, we describe multiclass classification as an encoding task, i.e. a process for building compression schemes that convert large “files” (feature vectors) into small……
Introduction: Interior design and home decoration involve a high amount of guesswork. In addition to the visual appearance of each individual item, the arrangement of these items in a room and how well they fit together are also highly important. These considerations combine to create the notoriously subjective concept of……
