Proximity Features: Privacy-Compliant Cold-Start Personalization at Airbnb

2026-07-13

AI Summary

This study addresses the challenge of personalizing recommendations for new or unauthenticated users in Airbnb’s cold-start scenarios, particularly under stringent privacy regulations that prohibit the use of persistent individual identifiers. To overcome this limitation, the authors propose a geolocation-based adaptive clustering method that dynamically aggregates approximately 1,000 proximate users into cohorts, thereby constructing group-level feature signals without relying on persistent user identities. This approach introduces a novel privacy-compliant mechanism driven by geographic proximity to enable effective personalization while preserving user anonymity. Online A/B experiments demonstrate that the proposed method significantly improves booking conversion rates, with especially pronounced gains for users exhibiting no prior activity or outdated behavioral data. The solution has been successfully deployed in production systems, including marketing landing pages and destination recommendation modules.

Abstract

Personalization in two-sided marketplaces relies heavily on user-level features, yet for platforms with infrequent, high-consideration purchases, a large fraction of users lack sufficient history for effective recommendation, spanning both paid and organic channels. At Airbnb, a substantial share of search requests comes from logged-out or first-time users, with this challenge especially pronounced on paid-channel landing pages, leaving traditional user-level features unavailable for a large fraction of traffic. Privacy regulations and increasing restrictions on third-party cookies further limit identifier-based tracking for non-essential use cases. This paper introduces Proximity Features, a privacy-compliant feature system that groups users by geographic proximity using geo-IP data and an adaptive clustering algorithm, producing aggregated user-level signals for groups of approximately 1,000 nearby users without requiring a persistent individual identifier at inference time. Privacy is preserved by design: the pipeline operates on consented, aggregated data only within consent-gated privacy controls. The system is deployed in production at Airbnb, serving multiple surfaces including marketing landing pages and destination recommendation, with engagement emails integration under way. Online A/B experiments demonstrate statistically significant lifts in bookings, with the largest gains observed among users with absent or stale history.

Problem

• cold-start
• personalization
• privacy compliance
• two-sided marketplaces
• user-level features

Innovation

• Proximity Features
• privacy-compliant personalization
• cold-start recommendation
• geographic clustering
• aggregated user signals