  Click-Through Rate (CTR) prediction holds a pivotal place in online
advertising and recommender systems since CTR prediction performance directly
influences the overall satisfaction of the users and the revenue generated by
companies. Even so, CTR prediction is still an active area of research since it
involves accurately modelling the preferences of users based on sparse and
high-dimensional features where the higher-order interactions of multiple
features can lead to different outcomes.
  Most CTR prediction models have relied on a single fusion and interaction
learning strategy. The few CTR prediction models that have utilized multiple
interaction modelling strategies have treated each interaction to be
self-contained. In this paper, we propose a novel model named STEC that reaps
the benefits of multiple interaction learning approaches in a single unified
architecture. Additionally, our model introduces residual connections from
different orders of interactions which boosts the performance by allowing lower
level interactions to directly affect the predictions. Through extensive
experiments on four real-world datasets, we demonstrate that STEC outperforms
existing state-of-the-art approaches for CTR prediction thanks to its greater
expressive capabilities.
