  Feature embedding learning and feature interaction modeling are two crucial
components of deep models for Click-Through Rate (CTR) prediction. Most
existing deep CTR models suffer from the following three problems. First,
feature interactions are either manually designed or simply enumerated. Second,
all the feature interactions are modeled with an identical interaction
function. Third, in most existing models, different features share the same
embedding size which leads to memory inefficiency. To address these three
issues mentioned above, we propose Automatic Interaction Machine (AIM) with
three core components, namely, Feature Interaction Search (FIS), Interaction
Function Search (IFS) and Embedding Dimension Search (EDS), to select
significant feature interactions, appropriate interaction functions and
necessary embedding dimensions automatically in a unified framework.
Specifically, FIS component automatically identifies different orders of
essential feature interactions with useless ones pruned; IFS component selects
appropriate interaction functions for each individual feature interaction in a
learnable way; EDS component automatically searches proper embedding size for
each feature. Offline experiments on three large-scale datasets validate the
superior performance of AIM. A three-week online A/B test in the recommendation
service of a mainstream app market shows that AIM improves DeepFM model by 4.4%
in terms of CTR.
