Learning market dynamics to power optimal pricing at airbnb

With the emergence of numerous online marketplaces in recent years, the need for efficient matching algorithms which balance supply and demand has grown significantly. ,. Airbnb is one such marketplace which tries to match guests and hosts. Guests searching on Airbnb are often looking for the best deals that they can get on their trip. Hosts, on the other hand, look to maximize their earnings by pricing optimally. To meet these diverging objectives, Airbnb employs optimal pricing algorithms to match guests with hosts. The challenge, however, is that unlike commoditized accommodations in the hotel industry, every listing on Airbnb is unique. Airbnb homes span over a broad spectrum, from price, location, quality, to size, etc. Analogously, guests search with custom price elasticities and preferences in mind. This heterogeneity brings challenges for personalization. In this talk, we highlight the some of the techniques that try to address these challenges and improve marketplace conversion. We also provide a framework to elements of machine learning with structural modeling in order to address problems related to optimal pricing. We will dive deeper into location systems that were developed with transfer learning in mind, helping address challenges of scale and dimensionality. We will also take a look at arrival lead time distributions which form a key part of demand models and pricing strategies.
Who is this presentation for?

Prerequisite knowledge:

What you'll learn?

Sharan Srinivasan is a machine learning engineer at Airbnb. He works on problems relating to modeling marketplace dynamics - pricing and search, optimal pricing strategies, search and location systems. Previously, Sharan worked on various data products at InSnap, a mobile hyper personalization company. He holds an MS from Stanford University, where he focused on operations research.