  Click-Through Rate (CTR) prediction, whose aim is to predict the probability
of whether 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 of CTR
prediction, a key to making effective prediction is to model high-order feature
interaction. An efficient way to do this is to perform inner product of feature
embeddings with self-attentive neural networks. To better model complex feature
interaction, in this paper we propose a novel DisentanglEd Self-atTentIve
NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the
computation of unary feature importance from pairwise interaction.
Specifically, the unary term models the general importance of one feature on
all other features, whereas the pairwise interaction term contributes to
learning the pure impact for each feature pair. We conduct extensive
experiments using two real-world benchmark datasets. The results show that
DESTINE not only maintains computational efficiency but achieves consistent
improvements over state-of-the-art baselines.
