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Data sparsity and cold-start problems are persistent challenges inrecommendation systems. Cross-domain recommendation (CDR) is a promisingsolution that utilizes knowledge from the source domain to improve therecommendation performance in the target domain. Previous CDR approaches havemainly followed the Embedding and Mapping (EMCDR) framework, which involveslearning a mapping function to facilitate knowledge transfer. However, theseapproaches necessitate re-engineering and re-training the network structure toincorporate transferrable knowledge, which can be computationally expensive andmay result in catastrophic forgetting of the original knowledge. In this paper,we present a scalable and efficient paradigm to address data sparsity andcold-start issues in CDR, named CDR-Adapter, by decoupling the originalrecommendation model from the mapping function, without requiringre-engineering the network structure. Specifically, CDR-Adapter is a novelplug-and-play module that employs adapter modules to align featurerepresentations, allowing for flexible knowledge transfer across differentdomains and efficient fine-tuning with minimal training costs. We conductedextensive experiments on the benchmark dataset, which demonstrated theeffectiveness of our approach over several state-of-the-art CDR approaches.

CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models