Open AccessA CTR Prediction Model With Double Matrix-Level Cross-Features
Author(s) -
Wei Zhang,
Yahui Han,
Zhaobin Kang,
Kaiyuan Qu
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3211656
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.