Cross-domain recommendation (CDR) has demonstrated to be an effective solution for alleviating the user cold-start issue. By leveraging rich user-item interactions available in a richly informative source domain, CDR could improve the recommendation performance for cold-start users in the target domain. Previous CDR approaches mostly adhere the Embedding and Mapping (EMCDR) paradigm, which learns a user-shared mapping function to transfer users' preference from the source domain to the target domain, neglecting users' personalized preference. Recent CDR approaches further leverage the meta-learning paradigm, considering the CDR task for each user independently and learning user-specific mapping functions for each user. However, they mostly learn representations for each user individually, which ignores the common preference between different users, neglecting valuable information for CDR. In addition, all these approaches usually summarize the user's preference into an overall representation, which can hardly capture the user's multi-interest preference. To this end, we propose a personalized multi-interest modeling framework for CDR to cold-start users, termed as NF-NPCDR. Specifically, we propose a personalized preference encoder that enhances the neural process (NP) with the normalizing flow (NF) to convert the Gaussian (unimodal) distribution to a multimodal distribution, providing a novel way to capture the user's personalized multi-interest preference. Then, we propose a common preference encoder with a preference pool to capture the common preference between different users. Furthermore, we introduce a stochastic adaptive decoder to incorporate both the personalized and common preference for cold-start users, adaptively modulating both preference for better recommendation.

翻译：跨域推荐（CDR）已被证明是缓解用户冷启动问题的有效解决方案。通过利用丰富信息源域中的大量用户-物品交互数据，CDR能够提升目标域中冷启动用户的推荐性能。现有CDR方法大多遵循嵌入与映射（EMCDR）范式，即学习一个用户共享的映射函数将用户偏好从源域迁移至目标域，但忽略了用户的个性化偏好。近期CDR方法进一步引入元学习范式，独立考虑每个用户的CDR任务并为每个用户学习用户特定的映射函数。然而，这些方法通常为每个用户单独学习表征，忽视了不同用户之间的共性偏好，导致丢失对CDR有价值的信息。此外，这些方法通常将用户偏好归纳为单一整体表征，难以捕捉用户的多兴趣偏好。为此，我们提出面向冷启动用户的个性化多兴趣建模CDR框架NF-NPCDR。具体而言，我们提出个性化偏好编码器，通过归一化流（NF）增强神经过程（NP）能力，将高斯（单峰）分布转化为多峰分布，为捕捉用户个性化多兴趣偏好提供新途径。随后，我们提出包含偏好池的共性偏好编码器，以捕获不同用户间的共性偏好。此外，我们引入随机自适应解码器，融合冷启动用户的个性化偏好与共性偏好，通过自适应调节两种偏好实现更优推荐。