A CTR Prediction Model With Double Matrix-Level Cross-Features

Abstract

CTR prediction is an important task in recommender systems, which is used to estimate the likelihood of a user clicking on an advertisement. In the past, the CTR prediction model based on the deep neural network mainly obtains the implicit feature combination of the model at the bit-wise level, and the interpretability and generalization of the model are poor. At the same time, the prediction accuracy of the model is poor. For the above problems, We propose a click-through rate prediction model (DTM) with double matrix-level cross-features. The model integrates various components such as multi-head self-attention, residual network and interaction network into an end-to-end model, and automatically obtains explicit feature combinations at the vector-wise level and bit-wise level, which not only has better interpretability, generalization and memory, and reduce the inherent flaws and engineering complexity of multi-modules. The experimental results show that on the datasets Criteo and Avazu, compared with other state-of-the-art CTR prediction models, the AUC values of the DTM model are increased by 4% and 3% on average, and the loss values are decreased by 3.5% and 2.8% on average, respectively.