通过用户行为预测客服需求，优化房源匹配排序。
Predicting Potential Customer Support Needs and Optimizing Search Ranking in a Two-Sided Marketplace
- 基于房客与房东行为数据构建支持需求预测模型。
- 模型接入搜索排名，使需客服介入的订单减少显著。
- 适合关注平台体验优化与智能匹配的从业者。
Airbnb 是一个连接房主与旅客的在线交易平台。当旅客入住通过 Airbnb 预订的房源时，仅有少量预订会产生需要 Airbnb 客户支持（CS）的情况，这不仅影响用户体验，也消耗平台资源。本文表明，通过分析房主与旅客的行为特征，可有效预测客服支持需求。我们构建了一个模型，用于预测每对房主与旅客匹配时产生支持需求的可能性，并将该预测得分作为搜索排名算法中的一个因素。这一调整提升了搜索结果中匹配的可靠性，显著降低了需要客服介入的预订比例。
原文摘要 · Abstract (English)
Airbnb is an online marketplace that connects hosts and guests to unique stays and experiences. When guests stay at homes booked on Airbnb, there are a small fraction of stays that lead to support needed from Airbnb's Customer Support (CS), which may cause inconvenience to guests and hosts and require Airbnb resources to resolve. In this work, we show that instances where CS support is needed may be predicted based on hosts and guests behavior. We build a model to predict the likelihood of CS support needs for each match of guest and host. The model score is incorporated into Airbnb's search ranking algorithm as one of the many factors. The change promotes more reliable matches in search results and significantly reduces bookings that require CS support.
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