How we built and iterated on a machine learning Search Ranking platform for a new two-sided marketplace and how we helped itgrow. Airbnb Experiences are handcrafted activities designed and led by expert hosts that offer a unique taste of local scene and culture. Each experience is vetted for quality by a team of editors before it makes its way onto the platform.
We launched Airbnb Experiences in November 2016 with 500 Experiences in 12 cities worldwide. During 2017, we grew the business to 5,000 Experiences in 60 cities. In 2018, the rapid growth continued, and we managed to bring Experiences to more than 1,000 destinations, including unique places like Easter Island, Tasmania, and Iceland.
We finished the year strong with more than 20,000 active Experiences. As the number of Experiences grew, Search & Discoverability as well as Personalization have become very important factors for the growth and success of the marketplace. In this blog post, we describe the stages of our Experience Ranking development using machine learning at different growth phases of the marketplace, from small to mid-size and large.
The main take-away is that machine learning-based Search Ranking works at every stage, given that we pick the model and infrastructure with the right level of complexity for the amount of data available and the size of the inventory that needs to be ranked. Very complex models will not work well when trained with small amounts of data, and simple baselines are sub-optimal when large amounts of training data are available.