邻近特征：爱彼迎（Airbnb）符合隐私规定的冷启动个性化推荐
Proximity Features: Privacy-Compliant Cold-Start Personalization at Airbnb
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中文总结 AI 辅助
针对爱彼迎冷启动个性化推荐难题，引入基于地理IP数据和自适应聚类算法的邻近特征系统，无需个人标识符，在保护隐私的同时实现聚合用户信号生成，经实验验证能显著提升预订量。
中文摘要 AI 辅助
双边市场中的个性化推荐严重依赖用户层面的特征。然而，对于爱彼迎（Airbnb）这类购买频率低、决策时间长的平台，很大一部分用户缺乏足够历史数据用于有效推荐。本文引入邻近特征，这是一个符合隐私规定的特征系统，利用地理IP数据和自适应聚类算法按地理邻近性对用户分组，在推理时无需持久的个人标识符就能为约1000个附近用户组生成聚合用户层面信号。该系统已在爱彼迎生产环境中部署，在线A/B实验表明预订量有显著提升，在历史数据缺失或过时的用户中收益最大。
英文摘要
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.
发表机构
- Airbnb Inc.（爱彼迎公司）
机构由 AI 辅助整理，请以论文原文为准。
