How Wayfair’s Scientists Collaborated with Innovative Startup ThirdAI to Serve Hyper-Relevant Search Results to Customers
Wayfair has tens of millions of products that serve the needs of over 33 million customers. Each of our customers have nuanced preferences as they buy products to help them create their own unique sense of home. Our search and recommender systems play an important role in helping customers find these products as quickly and conveniently as possible.
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Wayfair’s New Approach to Aspect Based Sentiment Analysis Helps Customers Easily Find “Long Tail” Products
Over thirty million customers from around the world shop on Wayfair to look for furniture that helps them create their unique sense of home. Wayfair serves a variety of webpages catered to niche customer sub-segments and help customers discover new products – these include pages with advice for people buying “twin futons,” or “nursery chairs with removable cushions.”
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Wayfair paper accepted at RecSys 2022 applies state-of-the-art method to generate product recommendations
Style transfer: How Wayfair leverages customer browse activity in one furniture category to inform recommendations for a category they have never shopped before.
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I recently had the honor of speaking at EmTech Digital, MIT Technology Review’s signature conference on artificial intelligence (AI) and business leadership. This isn’t my first time speaking at the conference, but it is my first time doing it in person.
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When customers submit search queries, such as “ivory jute oriental,” it is essential for the search engine to understand which types of products they are looking for. In this case, the customer is likely shopping for an area rug. Equipped with this knowledge, we can tailor the search results page by displaying area rug-specific filters and banners and refine the search results by first showing area rugs. At Wayfair Search, the Query Classifier Model performs this task of real-time prediction of the desired product types based on user search queries. This blog post discusses how we designed, trained, and deployed this deep learning classification model at SearchTech.
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Customers’ tastes may change over a period of time. How can we leverage their browse history to make sure our recommendations reflect their most up-to-date preferences?
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Out of a zillion options, customers want to find the one item perfect for them. The recommendations team makes that happen by leveraging advanced machine learning techniques and our immense datasets to provide ever improving recommendations. A key step in this process is experimentation, i.e. the design, launch and analysis of A/B tests to understand which algorithm provides the best customer experience. This blog post describes a simulation tool our Analytics team has built and used prior to every test launch. This tool has improved our test success rate and testing velocity dramatically, and led to new insights/improvements of our recommendations.
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Two Birds, One Stone: Hedwig, A Random-Walk Based Algorithm for Substitutable and Complementary Furniture Recommendations
At Wayfair, we recommend millions of products across all styles and budgets. Because of the scale of our catalog, we constantly ask ourselves: how can we help our customers find the right products efficiently while discovering exciting complements to fill out their space?
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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 we have something for every style and home. However, the large size of our product catalog also makes it hard for customers to find the perfect item among all of the possible options.
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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.
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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.
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