  User response prediction, which aims to predict the probability that a user
will provide a predefined positive response in a given context such as clicking
on an ad or purchasing an item, is crucial to many industrial applications such
as online advertising, recommender systems, and search ranking. However, due to
the high dimensionality and super sparsity of the data collected in these
tasks, handcrafting cross features is inevitably time expensive. Prior studies
in predicting user response leveraged the feature interactions by enhancing
feature vectors with products of features to model second-order or high-order
cross features, either explicitly or implicitly. Nevertheless, these existing
methods can be hindered by not learning sufficient cross features due to model
architecture limitations or modeling all high-order feature interactions with
equal weights. This work aims to fill this gap by proposing a novel
architecture Deep Cross Attentional Product Network (DCAP), which keeps cross
network's benefits in modeling high-order feature interactions explicitly at
the vector-wise level. Beyond that, it can differentiate the importance of
different cross features in each network layer inspired by the multi-head
attention mechanism and Product Neural Network (PNN), allowing practitioners to
perform a more in-depth analysis of user behaviors. Additionally, our proposed
model can be easily implemented and train in parallel. We conduct comprehensive
experiments on three real-world datasets. The results have robustly
demonstrated that our proposed model DCAP achieves superior prediction
performance compared with the state-of-the-art models. Public codes are
available at https://github.com/zachstarkk/DCAP.
