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.

翻译：Airbnb 是一个连接房东与房客、提供独特住宿体验的在线双边市场。当房客入住通过 Airbnb 预订的房源时，有少量住宿会引发需要 Airbnb 客户支持介入的情况，这可能给房客和房东带来不便，并消耗 Airbnb 的资源以解决问题。本研究表明，客户支持需求的发生可根据房东和房客的行为特征进行预测。我们构建了一个模型，用于预测每次房东与房客匹配后需要客户支持的可能性。该模型的评分被纳入 Airbnb 的搜索排序算法，作为众多排序因子之一。这一调整提升了搜索结果中匹配的可靠性，并显著减少了需要客户支持的预订量。