Research on CTR Prediction Based on Deep Learning

Abstract

Click-through rate (CTR) prediction is critical in Internet advertising and affects web publisher's profits and advertiser's payment. In the CTR prediction, the interaction between features is a key factor affecting the prediction rate. The traditional method of obtaining features using feature extraction did not consider the sparseness of advertising data and the highly nonlinear association between features. To reduce the sparseness of data and to mine the hidden features in advertising data, a method that learns the sparse features is proposed. Our method exploits dimension reduction based on decomposition and combines the power of field-aware factorization machines and deep learning to portray the nonlinear associated relationship of data to solve the sparse feature learning problem. The experiment shows that our method improves the effect of CTR prediction and produces economic benefits in Internet advertising.

Keywords

• Click through rate
• deep learning
• factorization machines
• sponsored search
• tensor decomposition