Optimizing Airbnb Search Journey with Multi-task Learning
Chun How Tan Austin Chan Malay Haldar Jie Tang Xin Liu Mustafa Abdool Huiji Gao Liwei He Sanjeev Katariya
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.