  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.
