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,……
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……
Choosing between school and work can be an either-or proposition for many, whether for financial reasons or because there simply don’t seem to be enough hours in the day. When faced with this decision myself, I initially shared similar concerns. However, I soon found that at Wayfair this can be……
Introduction Uplift models predict the incremental effect of a treatment on the outcome of an individual. In other words, they answer the causal question of how an individual’s behavior will change if we give him/her a specific treatment. At Wayfair, we build uplift models to inform us of whom to……
The Lucas Critique, named after economist Robert Lucas, is a theoretical result that blew up the discipline of macroeconomics in the 1970s, and its implications are directly relevant to much of the work that data scientists are doing today—including work that I am doing on the Algorithms team at Wayfair!……
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……
