提出新模型A²DCDR，提升跨域推荐精准度。
Adversarial Alignment and Disentanglement for Cross-Domain CTR Prediction with Domain-Encompassing Features
- 用对抗训练优化MMD，更好对齐领域间特征
- 融合不变特征与未对齐特征，提升推荐效果
- 适合解决数据稀疏和冷启动场景的推荐系统
跨域推荐（CDR）被广泛研究以缓解数据稀疏和冷启动问题。现有方法通常分离共享的领域无关特征和各领域的特定特征，但仅依赖领域无关特征与目标域特定特征组合，常导致性能不佳。为此，本文提出对抗对齐与解耦跨域推荐模型（A²DCDR），通过三个关键组件捕捉更全面的跨域信息：利用对抗训练改进MMD以增强泛化能力；采用特征解耦与重构机制实现域内特征解耦；引入新型融合表示，结合领域无关特征、未对齐特征与原始上下文数据。在真实数据集及线上A/B测试中，A²DCDR优于现有方法，验证了其有效性和实用性。代码已公开于https://github.com/youzi0925/A-2DCDR/tree/main。
原文摘要 · Abstract (English)
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.
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