A Soft-partitioned Semi-supervised Collaborative Transfer Learning Approach for Multi-Domain Recommendation

• URL: http://arxiv.org/abs/2511.01404v1
• Date: Mon, 03 Nov 2025 09:58:32 GMT
• Title: A Soft-partitioned Semi-supervised Collaborative Transfer Learning Approach for Multi-Domain Recommendation
• Authors: Xiaoyu Liu, Yiqing Wu, Ruidong Han, Fuzhen Zhuang, Xiang Li, Wei Lin,
• Abstract summary: We propose Soft-partitioned Semi-supervised Collaborative Transfer Learning (SSCTL) for multi-domain recommendation.<n> SSCTL generates dynamic parameters to address the overwhelming issue, thus shifting focus towards samples from non-dominant domains.<n>Online tests yielded significant improvements across various domains, with increases in GMV ranging from 0.54% to 2.90% and enhancements in CTR ranging from 0.22% to 1.69%.
• Score: 33.21794937808597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In industrial practice, Multi-domain Recommendation (MDR) plays a crucial role. Shared-specific architectures are widely used in industrial solutions to capture shared and unique attributes via shared and specific parameters. However, with imbalanced data across different domains, these models face two key issues: (1) Overwhelming: Dominant domain data skews model performance, neglecting non-dominant domains. (2) Overfitting: Sparse data in non-dominant domains leads to overfitting in specific parameters. To tackle these challenges, we propose Soft-partitioned Semi-supervised Collaborative Transfer Learning (SSCTL) for multi-domain recommendation. SSCTL generates dynamic parameters to address the overwhelming issue, thus shifting focus towards samples from non-dominant domains. To combat overfitting, it leverages pseudo-labels with weights from dominant domain instances to enhance non-dominant domain data. We conduct comprehensive experiments, both online and offline, to validate the efficacy of our proposed method. Online tests yielded significant improvements across various domains, with increases in GMV ranging from 0.54% to 2.90% and enhancements in CTR ranging from 0.22% to 1.69%.

Related papers

• CDC: Causal Domain Clustering for Multi-Domain Recommendation [32.945861240561]
  Multi-domain recommendation leverages domain-general knowledge to improve recommendations across several domains.<n>Existing domain grouping methods, based on business logic or data similarities, often fail to capture the true transfer relationships required for optimal grouping.<n>We propose Causal Domain Clustering (CDC) to effectively cluster domains.<n>CDC significantly enhances performance across over 50 domains on public datasets and in industrial settings, achieving a 4.9% increase in online eCPM.
  arXiv  Detail & Related papers  (2025-07-09T14:15:47Z)
• Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation [51.74051587372479]
  Multi-Domain Recommendation (MDR) achieves the desirable recommendation performance by effectively utilizing the transfer information across different domains.<n>We propose a simple and dynamic Similar Domain Selection Principle (SDSP) for multi-domain recommendation in this paper.<n>We emphasize that SDSP is a lightweight method that can be incorporated with existing MDR methods for better performance while not introducing excessive time overheads.
  arXiv  Detail & Related papers  (2025-05-26T17:07:31Z)
• HAMUR: Hyper Adapter for Multi-Domain Recommendation [49.87140704564021]
  We propose a novel model Hyper Adapter for Multi-Domain Recommendation (HAMUR)<n>HamUR consists of two components.<n>HamUR implicitly captures shared information among domains and dynamically generates the parameters for the adapter.
  arXiv  Detail & Related papers  (2023-09-12T13:34:33Z)
• Adapting Self-Supervised Representations to Multi-Domain Setups [47.03992469282679]
  Current state-of-the-art self-supervised approaches, are effective when trained on individual domains but show limited generalization on unseen domains. We propose a general-purpose, lightweight Domain Disentanglement Module that can be plugged into any self-supervised encoder.
  arXiv  Detail & Related papers  (2023-09-07T20:05:39Z)
• Joint Attention-Driven Domain Fusion and Noise-Tolerant Learning for Multi-Source Domain Adaptation [2.734665397040629]
  Multi-source Unsupervised Domain Adaptation transfers knowledge from multiple source domains with labeled data to an unlabeled target domain. The distribution discrepancy between different domains and the noisy pseudo-labels in the target domain both lead to performance bottlenecks. We propose an approach that integrates Attention-driven Domain fusion and Noise-Tolerant learning (ADNT) to address the two issues mentioned above.
  arXiv  Detail & Related papers  (2022-08-05T01:08:41Z)
• Mixup Regularized Adversarial Networks for Multi-Domain Text Classification [16.229317527580072]
  Using the shared-private paradigm and adversarial training has significantly improved the performances of multi-domain text classification (MDTC) models. However, there are two issues for the existing methods. We propose a mixup regularized adversarial network (MRAN) to address these two issues.
  arXiv  Detail & Related papers  (2021-01-31T15:24:05Z)
• Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation [74.3349233035632]
  Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) do not consider an inter-class variation within the target domain itself or estimated category. We introduce a learnable clustering module, and a novel domain adaptation framework called cross-domain grouping and alignment. Our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
  arXiv  Detail & Related papers  (2020-12-15T11:36:21Z)
• Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation [66.74638960925854]
  Partial domain adaptation (PDA) deals with a realistic and challenging problem when the source domain label space substitutes the target domain. We propose an Adaptively-Accumulated Knowledge Transfer framework (A$2$KT) to align the relevant categories across two domains.
  arXiv  Detail & Related papers  (2020-08-27T00:53:43Z)
• Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation [56.94873619509414]
  Conventional unsupervised domain adaptation studies the knowledge transfer between a limited number of domains. We propose a novel Domain2Vec model to provide vectorial representations of visual domains based on joint learning of feature disentanglement and Gram matrix. We demonstrate that our embedding is capable of predicting domain similarities that match our intuition about visual relations between different domains.
  arXiv  Detail & Related papers  (2020-07-17T22:05:09Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.