Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendation (CDR), which leverages user interactions from one domain to improve prediction performance in another, has emerged as a promising solution. However, users with similar preferences in the source domain may exhibit different interests in the target domain. Therefore, directly transferring embeddings may introduce irrelevant source-domain collaborative information. In this paper, we propose a novel graph-based disentangled contrastive learning framework to capture fine-grained user intent and filter out irrelevant collaborative information, thereby avoiding negative transfer. Specifically, for each domain, we use a multi-channel graph encoder to capture diverse user intents. We then construct the affinity graph in the embedding space and perform multi-step random walks to capture high-order user similarity relationships. Treating one domain as the target, we propose a disentangled intent-wise contrastive learning approach, guided by user similarity, to refine the bridging of user intents across domains. Extensive experiments on four benchmark CDR datasets demonstrate that DisCo consistently outperforms existing state-of-the-art baselines, thereby validating the effectiveness of both DisCo and its components.

翻译：推荐系统广泛应用于各类现实应用中，但常面临用户冷启动问题的持续挑战。跨域推荐通过利用一个领域的用户交互来提升另一领域的预测性能，已成为一种颇具前景的解决方案。然而，在源领域具有相似偏好的用户可能在目标领域表现出不同的兴趣。因此，直接迁移嵌入向量可能引入无关的源领域协同信息。本文提出一种新颖的基于图的解耦对比学习框架，以捕捉细粒度用户意图并过滤无关协同信息，从而避免负迁移。具体而言，针对每个领域，我们采用多通道图编码器捕捉多样化的用户意图。随后在嵌入空间中构建亲和力图，并通过多步随机游走捕获高阶用户相似性关系。以某一领域为目标，我们提出一种解耦的意图级对比学习方法，在用户相似性指导下优化跨领域用户意图的桥接。在四个基准跨域推荐数据集上的大量实验表明，DisCo 始终优于现有最先进的基线方法，从而验证了 DisCo 及其各组成部分的有效性。