Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain. Traditional CDR studies follow the embedding and mapping (EMCDR) paradigm, which transfers user representations from the source to target domain by learning a user-shared mapping function, neglecting the user-specific preference. Recent CDR studies attempt to learn user-specific mapping functions in meta-learning paradigm, which regards each user's CDR as an individual task, but neglects the preference correlations among users, limiting the beneficial information for user representations. Moreover, both of the paradigms neglect the explicit user-item interactions from both domains during the mapping process. To address the above issues, this paper proposes a novel CDR framework with neural process (NP), termed as CDRNP. Particularly, it develops the meta-learning paradigm to leverage user-specific preference, and further introduces a stochastic process by NP to capture the preference correlations among the overlapping and cold-start users, thus generating more powerful mapping functions by mapping the user-specific preference and common preference correlations to a predictive probability distribution. In addition, we also introduce a preference remainer to enhance the common preference from the overlapping users, and finally devises an adaptive conditional decoder with preference modulation to make prediction for cold-start users with items in the target domain. Experimental results demonstrate that CDRNP outperforms previous SOTA methods in three real-world CDR scenarios.

翻译：跨领域推荐（CDR）已被证明是解决用户冷启动问题的一种有效途径，其核心在于通过迁移源领域推导出的用户偏好，为目标领域用户生成推荐。传统CDR研究遵循嵌入与映射（EMCDR）范式，通过学习用户共享的映射函数将用户表征从源领域迁移至目标领域，但忽略了用户特定的偏好信息。近期CDR研究尝试在元学习范式下构建用户特定的映射函数，将每个用户的跨领域推荐视为独立任务，却忽视了用户间的偏好关联性，限制了用户表征可获取的有效信息。此外，现有两种范式均未在映射过程中显式利用双领域的用户-项目交互信息。为解决上述问题，本文提出一种基于神经过程（NP）的新型跨领域推荐框架CDRNP。该框架在元学习范式基础上充分挖掘用户特定偏好，并引入神经过程构建随机过程以捕捉重叠用户与冷启动用户间的偏好关联，从而将用户特定偏好与群体偏好关联映射为预测概率分布，生成更具表达力的映射函数。此外，我们设计了偏好保持器以增强重叠用户的共性偏好表征，最终构建具有偏好调制功能的自适应条件解码器，为目标领域项目生成针对冷启动用户的预测。实验结果表明，CDRNP在三种真实跨领域推荐场景中均优于现有最优方法。