  Click-through rate (CTR) prediction, which aims to predict the probability
that whether of a user will click on an item, is an essential task for many
online applications. Due to the nature of data sparsity and high dimensionality
in CTR prediction, a key to making effective prediction is to model high-order
feature interactions among feature fields. To explicitly model high-order
feature interactions, an efficient way is to stack multihead self-attentive
neural networks, which has achieved promising performance. However, one problem
of the vanilla self-attentive network is that two terms, a whitened pairwise
interaction term and a unary term, are coupled in the computation of the
self-attention score, where the pairwise term contributes to learning the
importance score for each feature interaction, while the unary term models the
impact of one feature on all other features. We identify two factors, coupled
gradient computation and shared transformations, impede the learning of both
terms. To solve this problem, in this paper,we present a novel Disentangled
Self-Attentive neural Network (DSAN) model for CTR prediction, which
disentangles the two terms for facilitating learning feature interactions. We
conduct extensive experiments framework using two real-world benchmark
datasets. The results show that DSAN not only retains computational efficiency
but obtains performance improvements over state-of-the-art baselines.
