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User response prediction, which aims to predict the probability that a userwill provide a predefined positive response in a given context such as clickingon an ad or purchasing an item, is crucial to many industrial applications suchas online advertising, recommender systems, and search ranking. However, due tothe high dimensionality and super sparsity of the data collected in thesetasks, handcrafting cross features is inevitably time expensive. Prior studiesin predicting user response leveraged the feature interactions by enhancingfeature vectors with products of features to model second-order or high-ordercross features, either explicitly or implicitly. Nevertheless, these existingmethods can be hindered by not learning sufficient cross features due to modelarchitecture limitations or modeling all high-order feature interactions withequal weights. This work aims to fill this gap by proposing a novelarchitecture Deep Cross Attentional Product Network (DCAP), which keeps crossnetwork's benefits in modeling high-order feature interactions explicitly atthe vector-wise level. Beyond that, it can differentiate the importance ofdifferent cross features in each network layer inspired by the multi-headattention mechanism and Product Neural Network (PNN), allowing practitioners toperform a more in-depth analysis of user behaviors. Additionally, our proposedmodel can be easily implemented and train in parallel. We conduct comprehensiveexperiments on three real-world datasets. The results have robustlydemonstrated that our proposed model DCAP achieves superior predictionperformance compared with the state-of-the-art models. Public codes areavailable at https://github.com/zachstarkk/DCAP.

DCAP: Deep Cross Attentional Product Network for User Response Prediction