Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation

Multi-Domain Recommendation (MDR) achieves the desirable recommendation performance by effectively utilizing the transfer information across different domains. Despite the great success, most existing MDR methods adopt a single structure to transfer complex domain-shared knowledge. However, the beneficial transferring information should vary across different domains. When there is knowledge conflict between domains or a domain is of poor quality, unselectively leveraging information from all domains will lead to a serious Negative Transfer Problem (NTP). Therefore, how to effectively model the complex transfer relationships between domains to avoid NTP is still a direction worth exploring. To address these issues, we propose a simple and dynamic Similar Domain Selection Principle (SDSP) for multi-domain recommendation in this paper. SDSP presents the initial exploration of selecting suitable domain knowledge for each domain to alleviate NTP. Specifically, we propose a novel prototype-based domain distance measure to effectively model the complexity relationship between domains. Thereafter, the proposed SDSP can dynamically find similar domains for each domain based on the supervised signals of the domain metrics and the unsupervised distance measure from the learned domain prototype. 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. To the best of our knowledge, it is the first solution that can explicitly measure domain-level gaps and dynamically select appropriate domains in the MDR field. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method.

@article{wen2025_2505.20227,
  title={ Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation },
  author={ Yi Wen and Yue Liu and Derong Xu and Huishi Luo and Pengyue Jia and Yiqing Wu and Siwei Wang and Ke Liang and Maolin Wang and Yiqi Wang and Fuzhen Zhuang and Xiangyu Zhao },
  journal={arXiv preprint arXiv:2505.20227},
  year={ 2025 }
}