  Advertising and feed ranking are essential to many Internet companies such as
Facebook. Among many real-world advertising and feed ranking systems, click
through rate (CTR) prediction plays a central role. In recent years, many
neural network based CTR models have been proposed and achieved success such as
Factorization-Machine Supported Neural Networks, DeepFM and xDeepFM. Many of
them contain two commonly used components: embedding layer and MLP hidden
layers. On the other side, gating mechanism is also widely applied in many
research fields such as computer vision(CV) and natural language
processing(NLP). Some research has proved that gating mechanism improves the
trainability of non-convex deep neural networks. Inspired by these
observations, we propose a novel model named GateNet which introduces either
the feature embedding gate or the hidden gate to the embedding layer or hidden
layers of DNN CTR models, respectively. The feature embedding gate provides a
learnable feature gating module to select salient latent information from the
feature-level. The hidden gate helps the model to implicitly capture the
high-order interaction more effectively. Extensive experiments conducted on
three real-world datasets demonstrate its effectiveness to boost the
performance of various state-of-the-art models such as FM, DeepFM and xDeepFM
on all datasets.
