  Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce
platforms to attract users to place orders. E-coupons are the digital
equivalent of traditional paper coupons which provide customers with discounts
or gifts. One of the fundamental problems related is how to deliver e-coupons
with minimal cost while users' willingness to place an order is maximized. We
call this problem the coupon allocation problem. This is a non-trivial problem
since the number of regular users on a mature e-platform often reaches hundreds
of millions and the types of e-coupons to be allocated are often multiple. The
policy space is extremely large and the online allocation has to satisfy a
budget constraint. Besides, one can never observe the responses of one user
under different policies which increases the uncertainty of the policy making
process. Previous work fails to deal with these challenges. In this paper, we
decompose the coupon allocation task into two subtasks: the user intent
detection task and the allocation task. Accordingly, we propose a two-stage
solution: at the first stage (detection stage), we put forward a novel
Instantaneous Intent Detection Network (IIDN) which takes the user-coupon
features as input and predicts user real-time intents; at the second stage
(allocation stage), we model the allocation problem as a Multiple-Choice
Knapsack Problem (MCKP) and provide a computational efficient allocation method
using the intents predicted at the detection stage. We conduct extensive online
and offline experiments and the results show the superiority of our proposed
framework, which has brought great profits to the platform and continues to
function online.
