Disentanglement techniques used in collaborative filtering uncover interaction intents between nodes, improving the interpretability of node representations and enhancing recommendation performance. However, existing disentanglement methods still face two problems. First, they focus on local structural features derived from direct node interactions and overlook the comprehensive graph structure, which limits disentanglement accuracy. Second, the disentanglement process depends on backpropagation signals derived from recommendation tasks and lacks direct supervision, which may lead to biases and overfitting. To address these issues, we propose the Intent Propagation Contrastive Collaborative Filtering (IPCCF) algorithm. Specifically, we design a double helix message propagation framework to more effectively extract the deep semantic information of nodes, thereby improving the model's understanding of interactions between nodes. We also develop an intent message propagation method that incorporates graph structure information into the disentanglement process, thereby expanding the consideration scope of disentanglement. In addition, contrastive learning techniques are employed to align node representations derived from structure and intents, providing direct supervision for the disentanglement process, mitigating biases, and enhancing the model's robustness to overfitting. Experiments on three real data graphs illustrate the superiority of the proposed approach.

翻译：解耦技术用于协同过滤，能够揭示节点之间的交互意图，提高节点表示的可解释性并增强推荐性能。然而，现有的解耦方法仍面临两个问题。首先，它们专注于直接节点交互产生的局部结构特征，忽略了全局图结构，这限制了解耦的准确性。其次，解耦过程依赖于推荐任务产生的反向传播信号，缺乏直接监督，可能导致偏差和过拟合。为解决这些问题，我们提出意图传播对比协同过滤（IPCCF）算法。具体而言，我们设计了一个双螺旋消息传播框架，以更有效地提取节点的深层语义信息，从而提升模型对节点间交互的理解。我们还开发了一种意图消息传播方法，将图结构信息融入解耦过程，从而扩展了解耦的考虑范围。此外，采用对比学习技术对齐基于结构和意图导出的节点表示，为解耦过程提供直接监督，缓解偏差，并增强模型对过拟合的鲁棒性。在三个真实数据图上的实验表明了所提方法的优越性。