MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation

Junxiong Tong, Mingjia Yin, Hao Wang, Qiushi Pan, Defu Lian, Enhong Chen

Abstract: Traditional recommendation systems, relying on single-domain data, often struggle with sparse data or new user scenarios. Cross-domain Recommendation (CDR) 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 multi-view 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 recommendations’ 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. Our code is available at https://github.com/The-garden-of-sinner/MDAP.