Abstract：Click-through rate prediction（CTR）is a crucial task in online display advertising.The data involved in CTR prediction usually has multiple features，and the method of extracting and modeling important features greatly affects the accuracy of CTR prediction.The previous methods have the problem of information interference in the process of feature importance extraction.In order to solve this problem，a novel advertising click-through rate prediction model based on dynamic extraction of feature importance is proposed.The model introduces the gating mechanism into the CTR model to initially screen the importance of features.At the same time，it uses a squeezing extraction network to obtain the importance of features，and obtains the association information between the important features through bilinear interaction，and finally uses the hidden gating residual network learns high-level information interaction.Through extensive experiments on two real advertising data sets，it is proved that it can obtain better accuracy than the traditional click-through rate prediction model and the state-of-the-art prediction model based on deep learning.
2022, Vol. 43 
