DADIN: Domain Adversarial Deep Interest Network for Cross Domain
  Recommender Systems
Menglin Kong Muzhou Hou Shaojie Zhao Feng Liu Ri Su Yinghao Chen
Click-Through Rate (CTR) prediction is one of the main tasks of the
recommendation system, which is conducted by a user for different items to give
the recommendation results. Cross-domain CTR prediction models have been
proposed to overcome problems of data sparsity, long tail distribution of
user-item interactions, and cold start of items or users. In order to make
knowledge transfer from source domain to target domain more smoothly, an
innovative deep learning cross-domain CTR prediction model, Domain Adversarial
Deep Interest Network (DADIN) is proposed to convert the cross-domain
recommendation task into a domain adaptation problem. The joint distribution
alignment of two domains is innovatively realized by introducing domain
agnostic layers and specially designed loss, and optimized together with CTR
prediction loss in a way of adversarial training. It is found that the Area
Under Curve (AUC) of DADIN is 0.08% higher than the most competitive baseline
on Huawei dataset and is 0.71% higher than its competitors on Amazon dataset,
achieving the state-of-the-art results on the basis of the evaluation of this
model performance on two real datasets. The ablation study shows that by
introducing adversarial method, this model has respectively led to the AUC
improvements of 2.34% on Huawei dataset and 16.67% on Amazon dataset.