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TopShelf: How to Arrange a Page of Products, Not Just Rank Them in a List

TopShelf: How to Arrange a Page of Products, Not Just Rank Them in a List

Daniel Saunders Daniel Saunders May 5th, 2020

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,……

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The Visual Complements Model (ViCs): Complementary Product Recommendations From Visual Cues

The Visual Complements Model (ViCs): Complementary Product Recommendations From Visual Cues

Hantian Liu Hantian Liu March 16th, 2020

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……

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Bayesian Product Ranking at Wayfair

Bayesian Product Ranking at Wayfair

David J. Harris David J. Harris January 20th, 2020 Tom Croonenborghs Tom Croonenborghs January 20th, 2020

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……

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ReCNet: Deep Learning based Cross-class Recommendations at Wayfair

ReCNet: Deep Learning based Cross-class Recommendations at Wayfair

Weiqi Weng Weiqi Weng December 12th, 2019

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……

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

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

Avatar Ilkay Yildiz September 16th, 2019

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……

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Introducing Harmonia: Context-Aware Product Recommendation  From Room Images

Introducing Harmonia: Context-Aware Product Recommendation From Room Images

Ozan Tezcan Ozan Tezcan September 9th, 2019

Introduction When looking for a new piece of furniture to add to a room, customers generally consider two major points: the visual appearance of the new item, and its harmony with the existing furniture inside the room. Together, these two considerations define a customer’s style preferences. Wayfair’s vast product catalog……

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Wayfair DS Explains It All: Samuel Yusuf on Collaborative Filtering

Wayfair DS Explains It All: Samuel Yusuf on Collaborative Filtering

Ingrid Xu Ingrid Xu August 14th, 2019

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……

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Lynx: Identifying Wayfair Customers’ Functional Needs

Lynx: Identifying Wayfair Customers’ Functional Needs

Kurt Zimmer Kurt Zimmer July 17th, 2019

Introduction One of the core principles of marketing is that messaging customers about what they want or need is much stronger than messaging them about what they don’t. It’s especially important for Wayfair to identify our customers’ needs, since many of our core products are items that are purchased once……

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

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

Cole Zuber Cole Zuber June 10th, 2019

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……

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How We Detect Anomalies in Our Product Recommendations Metrics

How We Detect Anomalies in Our Product Recommendations Metrics

Avatar Dario Marrocchelli April 11th, 2019

Introduction If you are a Wayfair customer looking for some wall art to decorate your house, chances are you will land on our wall art SuperBrowse page (see image below or link here). This page shows a grid of 48 products, all of which are pieces of wall art. Those……

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The Biggest Ad Tech Company You’ve Never Heard Of: Ad Tech & Customer Intelligence Engineering at Wayfair

The Biggest Ad Tech Company You’ve Never Heard Of: Ad Tech & Customer Intelligence Engineering at Wayfair

Franklyn Tamalenus Franklyn Tamalenus April 1st, 2019

An introduction by way of some marketing prose. Cutting edge intelligent systems. Big data at scale. World class ad tech to drive phenomenal growth in a multi-billion dollar business. Real-time tracking and personalization to engage millions of shoppers daily: Ad Tech & Customer Intelligence Engineering @ Wayfair. Meet the two…

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