Abstract
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.
Abstract (translated)
跨域推荐旨在利用多个域的知识来减轻传统推荐系统数据匮乏和启动问题。一种流行的范式是使用重叠用户表示来建立域连接,从而在所有情况下改善推荐性能。然而,这种方法的普遍做法是分别训练每个域的用户嵌入,然后将它们简单地组合在一起,往往忽略了用户和物品之间的跨域相似之处。此外,考虑到它们的训练目标是以推荐任务为导向,但没有任何具体 Regularization,优化的嵌入 disregard 用户观点之间的 interest 对齐,甚至违反了用户的最初兴趣分布。为了解决这些挑战,我们提出了一种全新的跨域推荐框架,即 COAST,以改善两个域的推荐性能,通过感知实体之间的跨域相似性和对齐用户兴趣。具体来说,我们首先建立一个统一的跨域异质图形,并重新定义图卷积神经网络的消息传递机制,以捕捉跨域用户和物品之间的高级别相似性。旨在对齐用户兴趣,我们基于两个更细粒度的用户-用户和用户-物品兴趣不变性视角开发了更深入的见解,利用无监督和语义信号。我们进行了广泛的实验,从两个大型推荐数据集构建了两个任务。广泛的结果表明,COAST consistently 和 significantly outperforms 先进的跨域推荐算法和经典的单域推荐方法。
URL
https://arxiv.org/abs/2301.11467
