Kapil Gupta explains how Airbnb approaches the personalization of travelers’ booking experiences using machine learning. Kapil begins with a discussion of the cold start problem, when the data is limited, and shows how personalized features are used to accommodate wide differences in Airbnb’s traveler and host attributes. He then details how the company deploys models in production with real-time features.
Kapil Gupta is a data science leader at Airbnb in San Francisco, where he leads the data science team focused on launching new travel verticals like Experiences and establishing Airbnb as an end-to-end travel platform. In his time at the company, he has worked on many challenging machine learning and personalization problems in search, pricing, and risk. Previously, he worked at PayPal and Duff & Phelps. He holds a PhD in operations research from Georgia Tech and a BTech from the Indian Institute of Technology (IIT), Madras.
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