  Data sparsity and cold-start problems are persistent challenges in
recommendation systems. Cross-domain recommendation (CDR) is a promising
solution that utilizes knowledge from the source domain to improve the
recommendation performance in the target domain. Previous CDR approaches have
mainly followed the Embedding and Mapping (EMCDR) framework, which involves
learning a mapping function to facilitate knowledge transfer. However, these
approaches necessitate re-engineering and re-training the network structure to
incorporate transferrable knowledge, which can be computationally expensive and
may result in catastrophic forgetting of the original knowledge. In this paper,
we present a scalable and efficient paradigm to address data sparsity and
cold-start issues in CDR, named CDR-Adapter, by decoupling the original
recommendation model from the mapping function, without requiring
re-engineering the network structure. Specifically, CDR-Adapter is a novel
plug-and-play module that employs adapter modules to align feature
representations, allowing for flexible knowledge transfer across different
domains and efficient fine-tuning with minimal training costs. We conducted
extensive experiments on the benchmark dataset, which demonstrated the
effectiveness of our approach over several state-of-the-art CDR approaches.
