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.

翻译：在爱彼迎（Airbnb）这一住宿与体验在线平台上，客人往往需要花费数周时间浏览和比较多个房源，最终才提交预订请求。而每次预订请求仍可能在入住前被房东拒绝或取消。搜索旅程的长期性与探索性，以及平衡客人与房东双方偏好的需求，给爱彼迎的搜索排序带来了独特挑战。本文提出旅程排序器（Journey Ranker）——一种新型多任务深度学习模型架构，以应对上述挑战。该模型将客人的中间行为（无论正向还是负向）作为里程碑，更有效地推动客人向成功预订迈进；同时利用客人状态、搜索查询等上下文信息平衡客人与房东的偏好。其模块化可扩展设计包含四个职责清晰分离的模块，可便捷地应用于爱彼迎搜索排序之外的场景。我们通过离线与在线测试验证了旅程排序器的性能，并将其成功部署至爱彼迎四个不同产品的生产环境，显著提升了业务指标。