Click-Through Rate Prediction Combining Mutual Information Feature Weighting and Feature Interaction

IEEE Access

Shuang Han¹  Hongbin Dong¹  Xiaowei Wang¹ 

[1] College of Computer Science and Technology, Harbin Engineering University, Harbin, China;

关键词: Click-through rate prediction;    mutual information;    feature interaction;   

DOI  :  10.1109/ACCESS.2020.3034630

来源: DOAJ

【 摘 要 】

Click-through rate (CTR) prediction is an important issue in online advertising and recommendation systems. It is used to estimate the likelihood that a user will click on ads. The method used in the traditional CTR prediction task is to improve the prediction effect through a large number of feature engineering. However, these methods are time-consuming and laborious, and the construction process is not universal. Due to the sparse and high-dimensional characteristics of data features, it is necessary to measure the importance of sparse features and obtain efficient feature interactions. In this paper, a novel CTR model based on Mutual Information and Feature Interaction (MiFiNN) is proposed. First, the mutual information of each sparse feature and the click result is calculated as the weight of each sparse feature. Subsequently, an interactive method combining the outer product and inner product is constructed to carry out the feature interaction. Then, the resulting feature interactions and the dense features set of the original input are taken as DNN inputs. We verify the proposed model on four datasets. In addition, several widely known models are introduced for comparison. The experimental results indicate the superiority of the model.

【 授权许可】

Unknown