  Click-through rate (CTR) Prediction is a crucial task in personalized
information retrievals, such as industrial recommender systems, online
advertising, and web search. Most existing CTR Prediction models utilize
explicit feature interactions to overcome the performance bottleneck of
implicit feature interactions. Hence, deep CTR models based on parallel
structures (e.g., DCN, FinalMLP, xDeepFM) have been proposed to obtain joint
information from different semantic spaces. However, these parallel
subcomponents lack effective supervisory signals, making it challenging to
efficiently capture valuable multi-views feature interaction information in
different semantic spaces. To address this issue, we propose a simple yet
effective novel CTR model: Contrast-enhanced Through Network for CTR (CETN), so
as to ensure the diversity and homogeneity of feature interaction information.
Specifically, CETN employs product-based feature interactions and the
augmentation (perturbation) concept from contrastive learning to segment
different semantic spaces, each with distinct activation functions. This
improves diversity in the feature interaction information captured by the
model. Additionally, we introduce self-supervised signals and through
connection within each semantic space to ensure the homogeneity of the captured
feature interaction information. The experiments and research conducted on four
real datasets demonstrate that our model consistently outperforms twenty
baseline models in terms of AUC and Logloss.
