Cross-domain recommendation (CDR) has been increasingly explored to address data sparsity and cold-start issues. However, recent approaches typically disentangle domain-invariant features shared between source and target domains, as well as domain-specific features for each domain. However, they often rely solely on domain-invariant features combined with target domain-specific features, which can lead to suboptimal performance. To overcome the limitations, this paper presents the Adversarial Alignment and Disentanglement Cross-Domain Recommendation ($A^2DCDR$ ) model, an innovative approach designed to capture a comprehensive range of cross-domain information, including both domain-invariant and valuable non-aligned features. The $A^2DCDR$ model enhances cross-domain recommendation through three key components: refining MMD with adversarial training for better generalization, employing a feature disentangler and reconstruction mechanism for intra-domain disentanglement, and introducing a novel fused representation combining domain-invariant, non-aligned features with original contextual data. Experiments on real-world datasets and online A/B testing show that $A^2DCDR$ outperforms existing methods, confirming its effectiveness and practical applicability. The code is provided at https://github.com/youzi0925/A-2DCDR/tree/main.

翻译：跨域推荐（CDR）已被广泛探索以解决数据稀疏性和冷启动问题。然而，现有方法通常仅解耦源域与目标域共享的域不变特征以及各域特有的域特定特征，并主要依赖域不变特征与目标域特定特征的结合，这可能导致性能欠佳。为克服这些局限，本文提出对抗对齐与解耦跨域推荐（$A^2DCDR$）模型，该创新方法旨在捕获全面的跨域信息，包括域不变特征与有价值的非对齐特征。$A^2DCDR$模型通过三个关键组件增强跨域推荐：利用对抗训练改进最大均值差异（MMD）以提升泛化能力，采用特征解耦器与重构机制实现域内解耦，以及引入融合域不变特征、非对齐特征与原始上下文数据的新型融合表示。在真实数据集和在线A/B测试上的实验表明，$A^2DCDR$优于现有方法，验证了其有效性与实际适用性。代码发布于 https://github.com/youzi0925/A-2DCDR/tree/main。