Recommending Search Filters To Improve Conversions At Airbnb
Hao Li, Kedar Bellare, Siyu Yang, Sherry Chen, Liwei He, Stephanie Moyerman, Sanjeev Katariya

TL;DR
This paper presents a machine learning-based system for recommending search filters at Airbnb, significantly improving booking conversions by directly targeting lower-funnel goals and overcoming deployment challenges.
Contribution
It introduces a novel modeling framework for filter recommendations that enhances conversions and details its successful deployment and validation at Airbnb.
Findings
Achieved incremental booking conversion lifts through filter recommendations.
Validated effectiveness via online A/B testing and ablation studies.
Addressed cold start and serving constraints in real-world deployment.
Abstract
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…
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Taxonomy
TopicsSharing Economy and Platforms · Transportation and Mobility Innovations · Recommender Systems and Techniques
