Multi-scale and multi-channel neural network for click-through rate prediction

Abstract

Click-through rate (CTR) prediction aims to predict the probability of a user clicking on items and ads. Feature engineering plays an important role in improving the accuracy of CTR predict ion because features are normally multi-field. Recently, existing neural network based approaches mostly focus on the feature interactions in both low- and high-order. However, this methodology ignores salient features in both local and global contexts and diverse semantic features in various aspects. In this paper, we propose a novel framework called Multi-Scale and Multi-Channel neural network (MSMC) to learn the feature importance and feature semantics for enhancing CTR prediction. MSMC consists of two parallel modules: a salient feature encoder (SFE) and a diverse semantic feature encoder (DFE). The SFE employs an attentive global–local contexts module to extract the salient features. The DFE uses an attentive semantic module to aggregate the diverse semantic features. Then, a fusion function is adopted to adaptively combine features from the SFE and the DFE so as to obtain high-order feature interactions. Experimental results on two public real datasets illustrate the effectiveness of MSMC compared to the state-of-the-art CTR prediction methods. Our extensive analysis of MSMC shows how the salient features, diverse semantic features, and fusion function positively impact the performance of CTR prediction.