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

TL;DR

CDR-Adapter introduces a plug-and-play adapter module for cross-domain recommendation models, enabling efficient knowledge transfer and alleviating data sparsity and cold-start issues without re-engineering the entire network.

Contribution

The paper proposes a scalable, efficient adapter-based paradigm for CDR that decouples the original model from transfer learning, reducing re-engineering and training costs.

Findings

Outperforms state-of-the-art CDR methods on benchmark datasets.

Requires minimal re-training and re-engineering.

Effectively alleviates cold-start and data sparsity problems.

Abstract

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.