  Cross-domain Recommendation (CR) is the task that tends to improve the
recommendations in the sparse target domain by leveraging the information from
other rich domains. Existing methods of cross-domain recommendation mainly
focus on overlapping scenarios by assuming users are totally or partially
overlapped, which are taken as bridges to connect different domains. However,
this assumption does not always hold since it is illegal to leak users'
identity information to other domains. Conducting Non-overlapping MCR (NMCR) is
challenging since 1) The absence of overlapping information prevents us from
directly aligning different domains, and this situation may get worse in the
MCR scenario. 2) The distribution between source and target domains makes it
difficult for us to learn common information across domains. To overcome the
above challenges, we focus on NMCR, and devise MCRPL as our solution. To
address Challenge 1, we first learn shared domain-agnostic and domain-dependent
prompts, and pre-train them in the pre-training stage. To address Challenge 2,
we further update the domain-dependent prompts with other parameters kept fixed
to transfer the domain knowledge to the target domain. We conduct experiments
on five real-world domains, and the results show the advance of our MCRPL
method compared with several recent SOTA baselines.
