  Cross-domain recommendation (CDR) extends conventional recommender systems by
leveraging user-item interactions from dense domains to mitigate data sparsity
and the cold start problem. While CDR offers substantial potential for
enhancing recommendation performance, most existing CDR methods suffer from
sensitivity to the ratio of overlapping users and intrinsic discrepancy between
source and target domains. To overcome these limitations, in this work, we
explore the application of graph signal processing (GSP) in CDR scenarios. We
propose CGSP, a unified CDR framework based on GSP, which employs a
cross-domain similarity graph constructed by flexibly combining target-only
similarity and source-bridged similarity. By processing personalized graph
signals computed for users from either the source or target domain, our
framework effectively supports both inter-domain and intra-domain
recommendations. Our empirical evaluation demonstrates that CGSP consistently
outperforms various encoder-based CDR approaches in both intra-domain and
inter-domain recommendation scenarios, especially when the ratio of overlapping
users is low, highlighting its significant practical implication in real-world
applications.
