Automated Prompting for Non-Overlapping Cross-Domain Sequential Recommendation

作者：Lei Guo, Chunxiao Wang, Xinhua Wang, Lei Zhu, Hongzhi Yin · 发表于：IEEE Transactions on Knowledge and Data Engineering · 年份：2025 · DOI：10.1109/tkde.2025.3589721 · 被引用次数：4 · 研究领域：Recommender Systems and Techniques、Advanced Bandit Algorithms Research、Expert finding and Q&A systems

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. Non-overlapping Cross-domain Sequential Recommendation (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 Cross-domain Recommendation (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), Prompt Learning-based Cross-domain Recommender (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 introdu...