Managing Diversity in Airbnb Search
This addresses the challenge of balancing relevance and diversity in search systems for Airbnb users, though it appears incremental as it builds on existing methods.
The paper tackled the problem of diversity in Airbnb search results, moving from heuristic approaches to a novel deep learning solution using RNNs to embed the entire query context.
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.