  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.

Information Retrieval