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Click-Through Rate (CTR) prediction is one of the main tasks of therecommendation system, which is conducted by a user for different items to givethe recommendation results. Cross-domain CTR prediction models have beenproposed to overcome problems of data sparsity, long tail distribution ofuser-item interactions, and cold start of items or users. In order to makeknowledge transfer from source domain to target domain more smoothly, aninnovative deep learning cross-domain CTR prediction model, Domain AdversarialDeep Interest Network (DADIN) is proposed to convert the cross-domainrecommendation task into a domain adaptation problem. The joint distributionalignment of two domains is innovatively realized by introducing domainagnostic layers and specially designed loss, and optimized together with CTRprediction loss in a way of adversarial training. It is found that the AreaUnder Curve (AUC) of DADIN is 0.08% higher than the most competitive baselineon 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 thismodel performance on two real datasets. The ablation study shows that byintroducing adversarial method, this model has respectively led to the AUCimprovements of 2.34% on Huawei dataset and 16.67% on Amazon dataset.

DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems