  Click-through rate (CTR) prediction, which aims to predict the probability of
a user clicking on an ad or an item, is critical to many online applications
such as online advertising and recommender systems. The problem is very
challenging since (1) the input features (e.g., the user id, user age, item id,
item category) are usually sparse and high-dimensional, and (2) an effective
prediction relies on high-order combinatorial features (\textit{a.k.a.} cross
features), which are very time-consuming to hand-craft by domain experts and
are impossible to be enumerated. Therefore, there have been efforts in finding
low-dimensional representations of the sparse and high-dimensional raw features
and their meaningful combinations. In this paper, we propose an effective and
efficient method called the \emph{AutoInt} to automatically learn the
high-order feature interactions of input features. Our proposed algorithm is
very general, which can be applied to both numerical and categorical input
features. Specifically, we map both the numerical and categorical features into
the same low-dimensional space. Afterwards, a multi-head self-attentive neural
network with residual connections is proposed to explicitly model the feature
interactions in the low-dimensional space. With different layers of the
multi-head self-attentive neural networks, different orders of feature
combinations of input features can be modeled. The whole model can be
efficiently fit on large-scale raw data in an end-to-end fashion. Experimental
results on four real-world datasets show that our proposed approach not only
outperforms existing state-of-the-art approaches for prediction but also offers
good explainability. Code is available at:
\url{https://github.com/DeepGraphLearning/RecommenderSystems}.
