  Sponsored search is a multi-billion dollar industry and makes up a major
source of revenue for search engines (SE). click-through-rate (CTR) estimation
plays a crucial role for ads selection, and greatly affects the SE revenue,
advertiser traffic and user experience. We propose a novel architecture for
solving CTR prediction problem by combining artificial neural networks (ANN)
with decision trees. First we compare ANN with respect to other popular machine
learning models being used for this task. Then we go on to combine ANN with
MatrixNet (proprietary implementation of boosted trees) and evaluate the
performance of the system as a whole. The results show that our approach
provides significant improvement over existing models.
