跨域推荐新框架，能自动分离并融合用户偏好，提升冷启动推荐效果。
MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation
- 多视角编码分离用户不同领域偏好，增强表达能力。
- 门控解码器动态选择最优特征组合，适应性强。
- 在多个数据集上优于现有方法，适合新用户和数据稀疏场景。
跨域推荐系统利用多领域用户交互信息提升性能，尤其在数据稀疏或新用户场景下表现更优。然而，现有方法面临用户偏好捕捉不充分与负迁移问题。为此，本文提出多视角解耦自适应偏好学习框架（MDAP）。该框架采用多视图编码器捕获用户在不同领域的多样化偏好，通过门控解码器自适应融合各视图嵌入，生成综合用户表示。通过解耦表示与动态特征选择机制，模型显著提升适应性与推荐效果。在多个基准数据集上的大量实验表明，该方法显著优于当前主流的跨域推荐与单域模型，不仅推荐精度更高，还为跨域用户行为分析提供了更深入洞察。
原文摘要 · Abstract (English)
Cross-domain Recommendation systems leverage multi-domain user interactions to improve performance, especially in sparse data or new user scenarios. However, CDR faces challenges such as effectively capturing user preferences and avoiding negative transfer. To address these issues, we propose the Multi-view Disentangled and Adaptive Preference Learning (MDAP) framework. Our MDAP framework uses a multiview encoder to capture diverse user preferences. The framework includes a gated decoder that adaptively combines embeddings from different views to generate a comprehensive user representation. By disentangling representations and allowing adaptive feature selection, our model enhances adaptability and effectiveness. Extensive experiments on benchmark datasets demonstrate that our method significantly outperforms state-of-the-art CDR and single-domain models, providing more accurate recommendations and deeper insights into user behavior across different domains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。