Using Neural Networks for Click Prediction of Sponsored Search

2014/12/20 by Afroze Ibrahim Baqapuri, Baqapuri, Afroze Ibrahim, Ilya Trofimov +1 · 1 citation
Computer Science · #Advanced Text Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Video Analysis and Summarization #Web Data Mining and Analysis

paper · pdf · doi:10.48550/arxiv.1412.6601

openalex publication_date 2014/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• User Response Prediction in Online Advertising

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