Adversarial Alignment and Disentanglement for Cross-Domain CTR Prediction with Domain-Encompassing Features

• URL: http://arxiv.org/abs/2601.17472v1
• Date: Sat, 24 Jan 2026 14:20:16 GMT
• Title: Adversarial Alignment and Disentanglement for Cross-Domain CTR Prediction with Domain-Encompassing Features
• Authors: Junyou He, Lixi Deng, Huichao Guo, Ye Tang, Yong Li, Sulong Xu,
• Abstract summary: Cross-domain recommendation (CDR) has been increasingly explored to address data sparsity and cold-start issues.<n>Recent approaches typically disentangle domain-invariant features shared between source and target domains, as well as domain-specific features for each domain.<n>This paper presents the Adversarial Alignment and Disentanglement Cross-Domain Recommendation model, an innovative approach designed to capture a comprehensive range of cross-domain information.
• Score: 9.70128576544644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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