  Recent E-commerce applications benefit from the growth of deep learning
techniques. However, we notice that many works attempt to maximize business
objectives by closely matching offline labels which follow the supervised
learning paradigm. This results in models obtain high offline performance in
terms of Area Under Curve (AUC) and Normalized Discounted Cumulative Gain
(NDCG), but cannot consistently increase the revenue metrics such as purchases
amount of users. Towards the issues, we build a simulated search engine AESim
that can properly give feedback by a well-trained discriminator for generated
pages, as a dynamic dataset. Different from previous simulation platforms which
lose connection with the real world, ours depends on the real data in
AliExpress Search: we use adversarial learning to generate virtual users and
use Generative Adversarial Imitation Learning (GAIL) to capture behavior
patterns of users. Our experiments also show AESim can better reflect the
online performance of ranking models than classic ranking metrics, implying
AESim can play a surrogate of AliExpress Search and evaluate models without
going online.
