Explicit Feature Interaction-aware Uplift Network for Online Marketing
Dugang Liu Xing Tang Han Gao Fuyuan Lyu Xiuqiang He
As a key component in online marketing, uplift modeling aims to accurately
capture the degree to which different treatments motivate different users, such
as coupons or discounts, also known as the estimation of individual treatment
effect (ITE). In an actual business scenario, the options for treatment may be
numerous and complex, and there may be correlations between different
treatments. In addition, each marketing instance may also have rich user and
contextual features. However, existing methods still fall short in both fully
exploiting treatment information and mining features that are sensitive to a
particular treatment. In this paper, we propose an explicit feature
interaction-aware uplift network (EFIN) to address these two problems. Our EFIN
includes four customized modules: 1) a feature encoding module encodes not only
the user and contextual features, but also the treatment features; 2) a
self-interaction module aims to accurately model the user's natural response
with all but the treatment features; 3) a treatment-aware interaction module
accurately models the degree to which a particular treatment motivates a user
through interactions between the treatment features and other features, i.e.,
ITE; and 4) an intervention constraint module is used to balance the ITE
distribution of users between the control and treatment groups so that the
model would still achieve a accurate uplift ranking on data collected from a
non-random intervention marketing scenario. We conduct extensive experiments on
two public datasets and one product dataset to verify the effectiveness of our
EFIN. In addition, our EFIN has been deployed in a credit card bill payment
scenario of a large online financial platform with a significant improvement.