Deep CTR Prediction in Display Advertising

TL;DR

This paper introduces a deep neural network model that predicts click-through rates for image ads directly from raw pixels and features, outperforming traditional logistic regression by automatically extracting visual features.

Contribution

The paper presents a novel DNN-based approach that directly predicts CTR from raw image pixels and features, enhancing feature extraction and prediction accuracy in display advertising.

Findings

Effective on large-scale dataset with over 50 million records

Automatically extracts visual features using convolutional layers

Outperforms traditional logistic regression models

Abstract

Click through rate (CTR) prediction of image ads is the core task of online display advertising systems, and logistic regression (LR) has been frequently applied as the prediction model. However, LR model lacks the ability of extracting complex and intrinsic nonlinear features from handcrafted high-dimensional image features, which limits its effectiveness. To solve this issue, in this paper, we introduce a novel deep neural network (DNN) based model that directly predicts the CTR of an image ad based on raw image pixels and other basic features in one step. The DNN model employs convolution layers to automatically extract representative visual features from images, and nonlinear CTR features are then learned from visual features and other contextual features by using fully-connected layers. Empirical evaluations on a real world dataset with over 50 million records demonstrate the effectiveness and efficiency of this method.