Cross-domain Recommendation is the task that tends to improve the recommendations in the sparse target domain by leveraging the information from other rich domains. Existing methods of cross-domain recommendation mainly focus on overlapping scenarios by assuming users are totally or partially overlapped, which are taken as bridges to connect different domains. However, this assumption does not always hold, since it is illegal to leak users’ identity information to other domains. Conducting Non-overlapping MCR (NMCR) is challenging, since (1) the absence of overlapping information prevents us from directly aligning different domains, and this situation may get worse in the MCR scenario, and (2) the distribution between source and target domains makes it difficult for us to learn common information across domains. To overcome the above challenges, we focus on NMCR and devise MCRPL as our solution. To address Challenge 1, we first learn shared domain-agnostic and domain-dependent prompts and pre-train them in the pre-training stage. To address Challenge 2, we further update the domain-dependent prompts with other parameters kept fixed to transfer the domain knowledge to the target domain. We conduct experiments on five real-world domains, and the results show the advance of our MCRPL method compared with several recent SOTA baselines. Moreover, our source codes have been publicly released.¹

跨域推荐（CR）是通过利用其他丰富域的信息来改进稀疏目标域中的推荐的任务。现有的跨域推荐方法主要针对重叠场景，假设用户完全或部分重叠，作为连接不同域的桥梁。然而，这种假设并不总是成立，因为将用户的身份信息泄露到其他域是非法的。进行非重叠 MCR (NMCR) 具有挑战性，因为 1) 缺乏重叠信息使我们无法直接对齐不同的域，并且这种情况在 MCR 场景中可能会变得更糟。2）源域和目标域之间的分布使得我们很难跨域学习公共信息。为了克服上述挑战，我们专注于 NMR，并设计了 MCRPL 作为我们的解决方案。为了解决挑战 1，我们首先学习共享的与领域无关和领域相关的提示，并在预训练阶段对它们进行预训练。为了解决挑战 2，我们进一步更新与领域相关的提示，其他参数保持固定，以将领域知识转移到目标领域。我们在五个现实世界领域进行了实验，结果显示了我们的 MCRPL 方法与最近的几个 SOTA 基线相比的进步。此外，我们的源代码已公开发布¹。