Recommending Search Filters To Improve Conversions At Airbnb

2026-02-27

AI Summary

This study addresses the lack of systematic modeling in existing literature on how search filters directly influence booking conversion rates. We propose the first search filter recommendation framework explicitly designed to optimize downstream conversion objectives. By leveraging machine learning, our approach establishes an explicit link between intermediate user interactions with filtering tools and final booking behavior, enabling personalized filter recommendations. The method innovatively aligns filter suggestions with business conversion goals while effectively addressing practical deployment challenges such as cold-start scenarios and high-concurrency traffic. Deployed across multiple Airbnb platforms, the system demonstrates significant improvements in booking conversion rates through rigorous A/B testing and ablation studies, thereby validating both the effectiveness and practical utility of the proposed framework.

Technology Category

• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization

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 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.

Problem

• search filters
• conversion
• online marketplace
• recommendation
• booking

Innovation

• filter recommendation
• conversion optimization
• machine learning
• online marketplace
• A/B testing