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有效途径，其目标是通过迁移源领域的用户偏好信息，为目标领域用户做出推荐。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示从源领域迁移到目标领域，但忽略了用户特定偏好。近期CDR研究尝试在元学习范式中学习用户特定映射函数，将每位用户的CDR视为独立任务，却忽视了用户之间的偏好相关性，限制了用户表示中的有益信息。此外，这两种范式在映射过程中均未考虑来自两个领域的显式用户-物品交互。为解决上述问题，本文提出一种基于神经过程（NP）的新型CDR框架，称为CDRNP。具体而言，该框架发展了元学习范式以利用用户特定偏好，并通过NP引入随机过程来捕获重叠用户与冷启动用户之间的偏好相关性，从而将用户特定偏好与公共偏好相关性映射为预测概率分布，生成更强大的映射函数。此外，我们引入偏好记忆器以增强来自重叠用户的公共偏好，并最终设计了一种带有偏好调制机制的自适应条件解码器，用于对目标领域中的冷启动用户进行物品预测。实验结果表明，CDRNP在三种真实跨领域推荐场景中均优于先前的最优方法（SOTA）。