Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. One popular paradigm is to employ overlapping user representations to establish domain connections, thereby improving recommendation performance in all scenarios. Nevertheless, the general practice of this approach is to train user embeddings in each domain separately and then aggregate them in a plain manner, often ignoring potential cross-domain similarities between users and items. Furthermore, considering that their training objective is recommendation task-oriented without specific regularizations, the optimized embeddings disregard the interest alignment among user's views, and even violate the user's original interest distribution. To address these challenges, we propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains by perceiving the cross-domain similarity between entities and aligning user interests. Specifically, we first construct a unified cross-domain heterogeneous graph and redefine the message passing mechanism of graph convolutional networks to capture high-order similarity of users and items across domains. Targeted at user interest alignment, we develop deep insights from two more fine-grained perspectives of user-user and user-item interest invariance across domains by virtue of affluent unsupervised and semantic signals. We conduct intensive experiments on multiple tasks, constructed from two large recommendation data sets. Extensive results show COAST consistently and significantly outperforms state-of-the-art cross-domain recommendation algorithms as well as classic single-domain recommendation methods.

翻译：跨域推荐旨在利用来自多个领域的知识，以缓解传统推荐系统中的数据稀疏性和冷启动问题。一种常见范式是通过重叠用户表示建立领域关联，从而提升所有场景下的推荐性能。然而，该方法的一般实践是分别在各个领域训练用户嵌入，再以简单方式进行聚合，往往忽略了用户与物品之间潜在跨域相似性。此外，考虑到其训练目标为面向推荐任务的非正则化优化，优化后的嵌入忽略了用户视角间的兴趣对齐，甚至违背了用户原始兴趣分布。为解决上述挑战，本文提出一种新型跨域推荐框架COAST，通过感知实体间跨域相似性与用户兴趣对齐，提升双领域推荐性能。具体而言，我们首先构建统一的跨域异构网络，重新定义图卷积网络的消息传递机制以捕获跨域用户与物品的高阶相似性。针对用户兴趣对齐，通过利用丰富的无监督与语义信号，从用户-用户和用户-物品跨域兴趣不变性这两个更细粒度的视角挖掘深层认知。我们在基于两个大型推荐数据集构建的多项任务上进行密集实验。广泛结果表明，COAST在一致且显著超越现有最优跨域推荐算法与经典单域推荐方法。