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.

该研究提出了一种名为 COAST 的新型跨领域推荐框架，通过感知实体之间的跨领域相似性和用户兴趣的对齐来提高双重领域的推荐性能，并使用用户表示和图卷积网络的消息传递机制来捕获用户和项目的高阶相似性，对跨领域推荐算法进行了广泛的实验，并证明 COAST 在多项任务中一致且显著优于同类算法。