Abstract: Cross-domain recommendation systems are widely applied in e-commerce and content platforms. Although the dual-target cross-domain recommendation (DTCDR) proposed in recent years has achieved a breakthrough in simultaneously improving the performance of both domains, it still faces two major challenges: 1) the generated user-item representations lack sufficient correlation and diversity; 2) the semantic noise mixed in the shared preferences leads to negative transfer problems. To address these issues, a dual-target cross-domain recommendation model based on heterogeneous graph and hierarchical preference disentanglement (HGPD-DTCDR) is proposed. Its core innovations include: 1) a heterogeneous graph collaborative learning framework is proposed to integrate user-item interactions, user social networks, and item attribute similarities, constructing a multi-relation heterogeneous graph, and generating high-order semantic representations through a relation graph convolutional network (R-GCN) to enhance the diversity and correlation of the representations; 2) a two-stage decoupling process is designed, first separating domain-specific and shared preferences through a variational graph encoder, and then introducing a semantic filtering network to optimize the quality of shared preferences. Experiments on five real cross-domain datasets show that the performance improvement of this model stems from the synergistic effect of heterogeneous graph modeling and hierarchical decoupling mechanisms. Compared with the best baseline, it achieves average improvements of 3.55%, 7.27%, and 15.57% in hit rate, normalized discounted cumulative gain, and mean reciprocal rank, respectively. In data-sparse scenarios, the performance improvement is even more significant, with an average gain of 10.35%. Ablation studies further verify the effectiveness of each technical component and their synergistic effects.

摘要： 跨域推荐系统在电商和内容平台应用广泛。近年来提出的双目标跨域推荐（DTCDR）虽突破性地实现了双领域性能同步提升，但仍面临两大挑战：1）生成的用户-项目表征相关性与多样性不足；2）共享偏好中混杂的语义噪声导致负迁移问题。为此，提出基于异构图和层次偏好解耦的双目标跨域推荐模型（HGPD-DTCDR），其核心创新包括：1）提出异构图协同学习框架，整合用户-项目交互、用户社交网络与项目属性相似性，构建多关系异构图，通过关系图卷积网络（R-GCN）生成高阶语义表征，增强表征的多样性与相关性；2）设计两阶段解耦流程，先通过变分图编码器分离领域共享与特定偏好，再引入语义过滤网络优化领域共享偏好质量。在五个真实跨域数据集上的实验表明，该模型的性能提升源于异构图建模和层次解耦机制的协同作用。相较于最优基线，在命中率、归一化折损累计增益指标和平均倒数排名上分别平均提升3.55%、7.27%和15.57%。在数据稀疏场景下，性能提升更为显著，平均增益达10.35%。消融研究进一步验证了各技术组件的有效性及其协同效应。