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

• URL: http://arxiv.org/abs/2607.12246v1
• Date: Tue, 14 Jul 2026 01:17:09 GMT
• Title: Proximity Features: Privacy-Compliant Cold-Start Personalization at Airbnb
• Abstract summary: This paper introduces Proximity Features, a privacy-compliant feature system that groups users by geographic proximity.<n>The pipeline operates on consented, aggregated data only within consent-gated privacy controls.<n>Online A/B experiments demonstrate statistically significant lifts in bookings, with the largest gains observed among users with absent or stale history.
• Score: 13.654796637146186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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