Using Neural Networks for Click Prediction of Sponsored Search
This work addresses a critical problem for search engines and advertisers in optimizing ad selection and revenue, but it appears incremental as it builds on existing methods.
The paper tackled click-through-rate prediction for sponsored search by proposing a hybrid architecture combining artificial neural networks with decision trees, resulting in significant improvement over existing models.
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.