Airbnb, a two-sided online marketplace connecting guests and hosts, offers a diverse and unique inventory of accommodations, experiences, and services. Search filters play an important role in helping guests navigate this variety by refining search results to align with their needs. Yet, while search filters are designed to facilitate conversions in online marketplaces, their direct impact on driving conversions remains underexplored in the existing literature. This paper bridges this gap by presenting a novel application of machine learning techniques to recommend search filters aimed at improving booking conversions. We introduce a modeling framework that directly targets lower-funnel conversions (bookings) by recommending intermediate tools, i.e. search filters. Leveraging the framework, we designed and built the filter recommendation system at Airbnb from the ground up, addressing challenges like cold start and stringent serving requirements. The filter recommendation system we developed has been successfully deployed at Airbnb, powering multiple user interfaces and driving incremental booking conversion lifts, as validated through online A/B testing. An ablation study further validates the effectiveness of our approach and key design choices. By focusing on conversion-oriented filter recommendations, our work ensures that search filters serve their ultimate purpose at Airbnb - helping guests find and book their ideal accommodations.

翻译：Airbnb是一个连接房客与房东的双边在线市场，提供多样化且独特的住宿、体验及服务资源。搜索过滤器通过筛选搜索结果以匹配用户需求，在帮助房客浏览多样化选择方面发挥着重要作用。然而，尽管搜索过滤器在设计上旨在促进在线市场的转化行为，现有文献对其直接驱动转化的影响仍缺乏深入探讨。本文通过提出一种新颖的机器学习技术应用来填补这一空白，该技术旨在通过推荐搜索过滤器以提升预订转化率。我们引入了一个建模框架，该框架通过推荐中间工具（即搜索过滤器）直接针对下游转化目标（预订行为）进行优化。基于此框架，我们从头设计并构建了Airbnb的过滤器推荐系统，解决了冷启动和严苛的服务要求等挑战。我们开发的过滤器推荐系统已在Airbnb成功部署，支持多个用户界面，并通过在线A/B测试验证了其带来的预订转化率提升。消融实验进一步验证了我们方法及关键设计决策的有效性。通过专注于以转化为导向的过滤器推荐，我们的工作确保了搜索过滤器在Airbnb实现其最终目标——帮助房客找到并预订理想住宿。