Proximity Features for Cold-Start Personalization

This post introduces Proximity Features, a new class of ML features that addresses the cold-start problem and evolving privacy constraints simultaneously. By grouping nearby users into buckets and aggregating their collective signals, rich, location-aware features can be constructed for any user whose IP can be geo-located, without relying on individual user identity. This enables personalization for logged-out, anonymous, or first-time users on marketing and acquisition surfaces, and can also benefit logged-in users. The system uses a proximity key based on geo-IP coordinates to group users into buckets of approximately 1,000, with an adaptive clustering algorithm that zooms in for dense areas and zooms out for sparse areas. Features include short-term engagement, long-term booking patterns, and aggregate bucket metadata. The system is designed with privacy in mind, excluding unconsented users and using coarse geo-IP aggregation. Proximity Features have launched across multiple Airbnb surfaces, showing measurable results in A/B experiments, including improved personalization for never-booked and dormant users on marketing landing pages and Homepage AutoSuggest.