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.

翻译：点击率（CTR）预测是推荐系统的主要任务之一，它通过用户对不同项目的操作来生成推荐结果。跨域CTR预测模型已被提出，以解决数据稀疏性、用户-项目交互的长尾分布以及项目或用户的冷启动问题。为了使知识从源域到目标域的迁移更加平滑，本文提出了一种创新的深度学习跨域CTR预测模型——域对抗深度兴趣网络（DADIN），将跨域推荐任务转化为域适应问题。通过引入域无关层和专门设计的损失函数，创新性地实现了两个域的联合分布对齐，并以对抗训练的方式与CTR预测损失联合优化。实验发现，在华为数据集上，DADIN的曲线下面积（AUC）比最具竞争力的基线高出0.08%，在亚马逊数据集上比竞争模型高出0.71%，在基于两个真实数据集评估模型性能的基础上取得了最先进的结果。消融研究表明，通过引入对抗方法，该模型在华为数据集和亚马逊数据集上分别实现了2.34%和16.67%的AUC提升。