Density Matrix Based Convolutional Neural Network for Click-Through Rate Prediction

Abstract

Click-Through Rate (CTR) prediction is an important task in the online advertisement recommended filed, which aims to predict the probabilities that an ad is clicked by a user in the display of the ads. Recently, many researchers have proposed deep learning based models to learn both low-order and high-order feature interactions. However, the existing deep learning based CTR models generally learn the feature interactions by inner product, which is simple and lack of interpretability. To address this issue, we introduce a concept of quantum probability theory: density matrix. Inspired by the feature of the density matrix, we propose a novel density matrix based representation for input instances, which can contain global 2-order feature interaction information. Then we combine the advantages of both density matrix and Convolutional Neural Network and propose the Density Matrix Based Convolutional Neural Network (DMCNN), which can capture more feature interactions than other models. Experiments on Criteo and Avazu datasets demonstrate the effectiveness of our model, which achieves excellent results on multiple benchmarks.