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.
Sam joined Wayfair’s Data Science team after earning an MS in Data Analytics from Georgia Institute of Technology. He loves solving complicated problems with data and figuring out the best way to recommend and sort through the millions of products that Wayfair sells. When he’s not exploring data, he loves exploring the woods, taking pictures, watching premier league soccer, playing guitar, biking, learning languages (human languages), and eating tacos! He was born in Lagos, Nigeria; grew up in Atlanta, GA; and thinks he can conquer/survive the New England winter.
