A Click Prediction Model Based on Residual Unit with Inception Module

Abstract

The explosion in online advertisement urges to better estimate the click prediction of ads. For click prediction on single ad impression, we have access to pairwise relevance among elements in an impression, but not to global interaction among key features of elements. Moreover, the existing method on sequential click prediction treats propagation unchangeable for different time intervals. In this work, we propose a novel model, Convolutional Click Prediction Model (RES-IN), based on residual unit with inception module. RES-IN can extract local-global key feature interactions from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression. Experiment results on three public large-scale datasets indicate that RES-IN is effective on click prediction.