Predicting Potential Customer Support Needs and Optimizing Search Ranking in a Two-Sided Marketplace

Do-kyum Kim, Han Zhao, Huiji Gao, Liwei He, Malay Haldar +1 more
3/21/2025
cs.LG

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Abstract

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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Cite this paper

@article{kim2025predicting,
  title  = {Predicting Potential Customer Support Needs and Optimizing Search Ranking in a Two-Sided Marketplace},
  author = {Do-kyum Kim and Han Zhao and Huiji Gao and Liwei He and Malay Haldar and Sanjeev Katariya},
  year   = {2025},
  eprint = {2503.17329},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.17329v1}
}

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