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.

本文介绍了Journey Ranker方法，它是一个新的多任务深度学习模型结构，可以解决Airbnb搜索排名中的独特挑战，包括为了平衡客户和主机的偏好而使用上下文信息的长时间探索之旅以及需要拒绝或取消预订请求等。经过离线和在线测试，Journey Ranker已成功部署在Airbnb的四个不同产品上，并显着提高了业务指标。

多任务学习优化Airbnb搜索旅程