  Predicting booking probability and value at the traveler level plays a
central role in computational advertising for massive two-sided vacation rental
marketplaces. These marketplaces host millions of travelers with long shopping
cycles, spending a lot of time in the discovery phase. The footprint of the
travelers in their discovery is a useful data source to help these marketplaces
to predict shopping probability and value. However, there is no
one-size-fits-all solution for this purpose. In this paper, we propose a hybrid
model that infuses deep and shallow neural network embeddings into a gradient
boosting tree model. This approach allows the latent preferences of millions of
travelers to be automatically learned from sparse session logs. In addition, we
present the architecture that we deployed into our production system. We find
that there is a pragmatic sweet spot between expensive complex deep neural
networks and simple shallow neural networks that can increase the prediction
performance of a model by seven percent, based on offline analysis.
