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

Using Neural Networks for Click Prediction of Sponsored Search