  The Click-though Rate (CTR) prediction task is a basic task in recommendation
system. Most of the previous researches of CTR models built based on Wide \&
deep structure and gradually evolved into parallel structures with different
modules. However, the simple accumulation of parallel structures can lead to
higher structural complexity and longer training time. Based on the Sigmoid
activation function of output layer, the linear addition activation value of
parallel structures in the training process is easy to make the samples fall
into the weak gradient interval, resulting in the phenomenon of weak gradient,
and reducing the effectiveness of training. To this end, this paper proposes a
Parallel Heterogeneous Network (PHN) model, which constructs a network with
parallel structure through three different interaction analysis methods, and
uses Soft Selection Gating (SSG) to feature heterogeneous data with different
structure. Finally, residual link with trainable parameters are used in the
network to mitigate the influence of weak gradient phenomenon. Furthermore, we
demonstrate the effectiveness of PHN in a large number of comparative
experiments, and visualize the performance of the model in training process and
structure.
