Accepted Papers

Managing Diversity in Airbnb Search

Mustafa Abdool: Airbnb; Malay Haldar: Airbnb; Prashant Ramanathan: Airbnb; Tyler Sax: Airbnb; Lanbo Zhang: Airbnb; Aamir Manasawala: Airbnb; Shulin Yang: Airbnb; Bradley Turnbull: Airbnb; Qing Zhang: Airbnb; Thomas Legrand: Airbnb


One of the long-standing questions in search systems is the role of diversity in results. From a product perspective, showing diverse results provides the user with more choice and should lead to an improved experience. However, this intuition is at odds with common machine learning approaches to ranking which directly optimize the relevance of each individual item without a holistic view of the result set. In this paper, we describe our journey in tackling the problem of diversity for Airbnb search, starting from heuristic based approaches and concluding with a novel deep learning solution that produces an embedding of the entire query context by leveraging Recurrent Neural Networks (RNNs). We hope our lessons learned will prove useful to others and motivate further research in this area.

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