Cross-domain Recommendation (CR) has been extensively studied in recent years to alleviate the data sparsity issue in recommender systems by utilizing different domain information. In this work, we focus on the more general Non-overlapping Cross-domain Sequential Recommendation (NCSR) scenario. NCSR is challenging because there are no overlapped entities (e.g., users and items) between domains, and there is only users' implicit feedback and no content information. Previous CR methods cannot solve NCSR well, since (1) they either need extra content to align domains or need explicit domain alignment constraints to reduce the domain discrepancy from domain-invariant features, (2) they pay more attention to users' explicit feedback (i.e., users' rating data) and cannot well capture their sequential interaction patterns, (3) they usually do a single-target cross-domain recommendation task and seldom investigate the dual-target ones. Considering the above challenges, we propose Prompt Learning-based Cross-domain Recommender (PLCR), an automated prompting-based recommendation framework for the NCSR task. Specifically, to address the challenge (1), PLCR resorts to learning domain-invariant and domain-specific representations via its prompt learning component, where the domain alignment constraint is discarded. For challenges (2) and (3), PLCR introduces a pre-trained sequence encoder to learn users' sequential interaction patterns, and conducts a dual-learning target with a separation constraint to enhance recommendations in both domains. Our empirical study on two sub-collections of Amazon demonstrates the advance of PLCR compared with some related SOTA methods.

翻译：跨域推荐（CR）近年来被广泛研究，旨在通过利用不同领域信息缓解推荐系统中的数据稀疏问题。本文聚焦于更一般的非重叠跨域序列推荐（NCSR）场景。NCSR具有挑战性，因为领域间无重叠实体（如用户和物品），且仅有用户隐式反馈而无内容信息。现有CR方法难以解决NCSR问题，原因在于：（1）它们要么需要额外内容进行领域对齐，要么需要显式领域对齐约束以减小域不变特征带来的领域差异；（2）它们更关注用户显式反馈（即用户评分数据），难以捕捉用户序列交互模式；（3）它们通常仅处理单目标跨域推荐任务，鲜少研究双目标场景。针对上述挑战，我们提出基于提示学习的跨域推荐器（PLCR），一种面向NCSR任务的自动化提示推荐框架。具体而言，为解决挑战（1），PLCR通过其提示学习组件学习域不变与域特定表示，并摒弃了领域对齐约束。针对挑战（2）和（3），PLCR引入预训练序列编码器学习用户序列交互模式，并采用带分离约束的双目标学习框架以增强双向域推荐。在Amazon两个子数据集上的实验表明，PLCR相比相关最先进方法具有显著优势。