Search & Recommender Systems

April 14, 2022
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.
3 Min Read
March 16, 2022
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.
8 Min Read
December 2, 2021
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?
7 Min Read
September 28, 2021
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.
10 Min Read
June 17, 2021
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?
9 Min Read
May 5, 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, followed by the second most likely product, and so forth. But shopkeepers in physical stores know that shelf arrangement is key, and that product appeal is not a fixed quality: it can actually change depending on the context in which a product appears. For example, placing a cheaper item next to an expensive item of the same category could show them both off to their best advantage, highlighting the thriftiness of one and the luxuriousness of the other. When shelves are perfectly arranged, even if some individual products sell less, the store as a whole will make more sales. This phenomenon has been studied in marketing psychology under the heading of context effects, including such phenomena as the attraction, compromise, and similarity effects.
8 Min Read
January 20, 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 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.
10 Min Read
August 14, 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 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.
1 Min Read
June 10, 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 Data Science’s explainer series, Senior Data Scientist Cole Zuber describes how we approach evaluating these recommender systems.
1 Min Read