  Click-Through Rate(CTR) estimation has become one of the most fundamental
tasks in many real-world applications and it's important for ranking models to
effectively capture complex high-order features. Shallow feed-forward network
is widely used in many state-of-the-art DNN models such as FNN, DeepFM and
xDeepFM to implicitly capture high-order feature interactions. However, some
research has proved that addictive feature interaction, particular feed-forward
neural networks, is inefficient in capturing common feature interaction. To
resolve this problem, we introduce specific multiplicative operation into DNN
ranking system by proposing instance-guided mask which performs element-wise
product both on the feature embedding and feed-forward layers guided by input
instance. We also turn the feed-forward layer in DNN model into a mixture of
addictive and multiplicative feature interactions by proposing MaskBlock in
this paper. MaskBlock combines the layer normalization, instance-guided mask,
and feed-forward layer and it is a basic building block to be used to design
new ranking model under various configurations. The model consisting of
MaskBlock is called MaskNet in this paper and two new MaskNet models are
proposed to show the effectiveness of MaskBlock as basic building block for
composing high performance ranking systems. The experiment results on three
real-world datasets demonstrate that our proposed MaskNet models outperform
state-of-the-art models such as DeepFM and xDeepFM significantly, which implies
MaskBlock is an effective basic building unit for composing new high
performance ranking systems.
