  Click Through Rate (CTR) prediction plays an essential role in recommender
systems and online advertising. It is crucial to effectively model feature
interactions to improve the prediction performance of CTR models. However,
existing methods face three significant challenges. First, while most methods
can automatically capture high-order feature interactions, their performance
tends to diminish as the order of feature interactions increases. Second,
existing methods lack the ability to provide convincing interpretations of the
prediction results, especially for high-order feature interactions, which
limits the trustworthiness of their predictions. Third, many methods suffer
from the presence of redundant parameters, particularly in the embedding layer.
This paper proposes a novel method called Gated Deep Cross Network (GDCN) and a
Field-level Dimension Optimization (FDO) approach to address these challenges.
As the core structure of GDCN, Gated Cross Network (GCN) captures explicit
high-order feature interactions and dynamically filters important interactions
with an information gate in each order. Additionally, we use the FDO approach
to learn condensed dimensions for each field based on their importance.
Comprehensive experiments on five datasets demonstrate the effectiveness,
superiority and interpretability of GDCN. Moreover, we verify the effectiveness
of FDO in learning various dimensions and reducing model parameters. The code
is available on \url{https://github.com/anonctr/GDCN}.
