  Learning feature interactions is crucial for click-through rate (CTR)
prediction in recommender systems. In most existing deep learning models,
feature interactions are either manually designed or simply enumerated.
However, enumerating all feature interactions brings large memory and
computation cost. Even worse, useless interactions may introduce noise and
complicate the training process. In this work, we propose a two-stage algorithm
called Automatic Feature Interaction Selection (AutoFIS). AutoFIS can
automatically identify important feature interactions for factorization models
with computational cost just equivalent to training the target model to
convergence. In the \emph{search stage}, instead of searching over a discrete
set of candidate feature interactions, we relax the choices to be continuous by
introducing the architecture parameters. By implementing a regularized
optimizer over the architecture parameters, the model can automatically
identify and remove the redundant feature interactions during the training
process of the model. In the \emph{re-train stage}, we keep the architecture
parameters serving as an attention unit to further boost the performance.
Offline experiments on three large-scale datasets (two public benchmarks, one
private) demonstrate that AutoFIS can significantly improve various FM based
models. AutoFIS has been deployed onto the training platform of Huawei App
Store recommendation service, where a 10-day online A/B test demonstrated that
AutoFIS improved the DeepFM model by 20.3\% and 20.1\% in terms of CTR and CVR
respectively.
