Optimizing Airbnb Search Journey with Multi-task Learning
This work addresses search ranking for Airbnb's marketplace, improving booking success by balancing guest and host preferences, though it is incremental as it builds on multi-task learning methods.
The paper tackled the challenge of optimizing Airbnb's search ranking due to the long, exploratory guest journey and the need to balance guest and host preferences, resulting in the deployment of Journey Ranker, a multi-task deep learning model, which improved business metrics across four products.
At Airbnb, an online marketplace for stays and experiences, guests often spend weeks exploring and comparing multiple items before making a final reservation request. Each reservation request may then potentially be rejected or cancelled by the host prior to check-in. The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking. In this paper, we present Journey Ranker, a new multi-task deep learning model architecture that addresses these challenges. Journey Ranker leverages intermediate guest actions as milestones, both positive and negative, to better progress the guest towards a successful booking. It also uses contextual information such as guest state and search query to balance guest and host preferences. Its modular and extensible design, consisting of four modules with clear separation of concerns, allows for easy application to use cases beyond the Airbnb search ranking context. We conducted offline and online testing of the Journey Ranker and successfully deployed it in production to four different Airbnb products with significant business metrics improvements.