Adversarial Alignment and Disentanglement for Cross-Domain CTR Prediction with Domain-Encompassing Features
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911396631937024 |
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| author | He, Junyou Deng, Lixi Guo, Huichao Tang, Ye Li, Yong Xu, Sulong |
| author_facet | He, Junyou Deng, Lixi Guo, Huichao Tang, Ye Li, Yong Xu, Sulong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17472 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adversarial Alignment and Disentanglement for Cross-Domain CTR Prediction with Domain-Encompassing Features He, Junyou Deng, Lixi Guo, Huichao Tang, Ye Li, Yong Xu, Sulong Information Retrieval 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. |
| title | Adversarial Alignment and Disentanglement for Cross-Domain CTR Prediction with Domain-Encompassing Features |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2601.17472 |