PeNet: A feature excitation learning approach to advertisement click-through rate prediction

Since the physical meaning of the fields of the dataset is unknown, we have to use the feature interaction method to select the correlated features and exclude uncorrelated features. The current state-of-the-art methods employ various methods based on feature interaction to predict advertisement Click-Through Rate (CTR); however, the feature interaction based on potential new feature mining is rarely considered, which can provide effective assistance for feature interaction. This motivates us to investigate methods that combine potential new features and feature interactions. Thus, we propose a potential feature excitation learning network (PeNet), which is a neural network model based on feature combination and feature interaction. In PeNet, we treat the row compression and column compression of the original feature matrix as potential new features, and proposed the excitation learning mechanism that is a weighted mechanism based on residual principle. Through this excitation learning mechanism, the original embedded features and potential new features are subjected to weighted interaction based on the residual principle. Moreover, a deep neural network is exploited to iteratively learn and iteratively combine features. The excitation learning structure of PeNet neural network is well demonstrated in this paper, that is, the control flow of embedding, compression, excitation and output, which further strengthens the correlated features and weakens the uncorrelated features by compressing and expanding the features. Experimental results on multiple benchmark datasets indicate the PeNet as a general-purpose plug-in has more superior performance and better efficiency than previous state-of-the-art methods.

Click-through Rate Prediction and Uncertainty Quantification Based on Bayesian Deep Learning

Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap in capturing uncertainty. Modeling uncertainty is a major challenge when using machine learning solutions to solve real-world problems in various domains. In order to quantify the uncertainty of the model and achieve accurate and reliable prediction results. This paper designs a CTR prediction framework combining feature selection and feature interaction. In this framework, a CTR prediction model based on Bayesian deep learning is proposed to quantify the uncertainty in the prediction model. On the squeeze network and DNN parallel prediction model framework, the approximate posterior parameter distribution of the model is obtained using the Monte Carlo dropout, and obtains the integrated prediction results. Epistemic and aleatoric uncertainty are defined and adopt information entropy to calculate the sum of the two kinds of uncertainties. Epistemic uncertainty could be measured by mutual information. Experimental results show that the model proposed is superior to other models in terms of prediction performance and has the ability to quantify uncertainty.