  Click-through rate (CTR) prediction is a critical task in recommendation
systems, serving as the ultimate filtering step to sort items for a user. Most
recent cutting-edge methods primarily focus on investigating complex implicit
and explicit feature interactions; however, these methods neglect the spurious
correlation issue caused by confounding factors, thereby diminishing the
model's generalization ability. We propose a CTR prediction framework that
REmoves Spurious cORrelations in mulTilevel feature interactions, termed
RE-SORT, which has two key components. I. A multilevel stacked recurrent (MSR)
structure enables the model to efficiently capture diverse nonlinear
interactions from feature spaces at different levels. II. A spurious
correlation elimination (SCE) module further leverages Laplacian kernel mapping
and sample reweighting methods to eliminate the spurious correlations concealed
within the multilevel features, allowing the model to focus on the true causal
features. Extensive experiments conducted on four challenging CTR datasets and
our production dataset demonstrate that the proposed method achieves
state-of-the-art performance in both accuracy and speed. The utilized codes,
models and dataset will be released at https://github.com/RE-SORT.
