Abstract: Cross-domain recommendation technique plays a crucial role in addressing the challenges of cold start and data sparsity in recommendation systems. However， existing methods often make the simplifying assumption that users’ preferences remain consistent across different domains. This assumption overlooks the inherent heterogeneity of user preferences， resulting in suboptimal recommendation performance. Furthermore， most approaches focus solely on recommendation tasks between domains， lacking a natural extension to multi-domain scenarios. In this spaper， we propose a novel approach called MAAH（Multi-Target Adversarial Cross-domain Recommendation based on Attributed Heterogeneous Graph）. The method leverages the structural information encoded in an attributed heterogeneous graph to represent users and items. By capturing both the homogeneity and heterogeneity of user behavior across domains， MAAH provides a more comprehensive understanding of user preferences. Importantly， we introduce adversarial learning to further integrate and discriminate user preferences， thereby enhancing recommendation effectiveness in each domain. Notably， MAAH achieves multi-objective cross-domain recommendation， addressing the limitations of existing methods. Experimental results on publicly available datasets demonstrate that MAAH effectively mitigates data sparsity and offers a promising solution to the cold start problem in cross-domain recommendation scenarios. 

Key words: recommendation system, cross-domain, attribute heterogeneity graph, graph embedding, adversarial learning