  The application of deep learning to search ranking was one of the most
impactful product improvements at Airbnb. But what comes next after you launch
a deep learning model? In this paper we describe the journey beyond, discussing
what we refer to as the ABCs of improving search: A for architecture, B for
bias and C for cold start. For architecture, we describe a new ranking neural
network, focusing on the process that evolved our existing DNN beyond a fully
connected two layer network. On handling positional bias in ranking, we
describe a novel approach that led to one of the most significant improvements
in tackling inventory that the DNN historically found challenging. To solve
cold start, we describe our perspective on the problem and changes we made to
improve the treatment of new listings on the platform. We hope ranking teams
transitioning to deep learning will find this a practical case study of how to
iterate on DNNs.
