SC-VUG: Fairness-Aware Cross-Domain Recommendation for Non-Overlapping Users via Sparsity-Calibrated Virtual User Generation

Cross-domain recommendation (CDR) leverages source domain knowledge to alleviate data sparsity in target domains. While existing methods have achieved significant success by utilizing overlapping users as bridges for knowledge transfer, they inadvertently introduce a critical fairness issue we term CDR Overlapping Bias : recommendation quality significantly improves for overlapping users but often stagnates or degrades for non-overlapping users. Furthermore, we observe that this disparity exacerbates as the ratio of overlapping users decreases, a common scenario in real-world applications. To address this inequity, we propose a novel model-agnostic framework named S parsity- C alibrated V irtual U ser G eneration (SC-VUG). SC-VUG synthesizes virtual source profiles for non-overlapping target users, effectively transforming them into virtual overlapping users to enable equitable knowledge transfer. Specifically, we design a Sparsity-Calibrated Generator that employs a dual-view attention mechanism to model virtual users as probabilistic distributions, where the variance explicitly captures generation uncertainty related to the overlapping ratio. To ensure the reliability of these synthesized profiles, we introduce a Structure-Preserving Limiter incorporating Anchor Loss and Isometry Loss, which aligns generated users with the source distribution while preserving the topological structure of user preferences. Comprehensive experiments conducted on three public datasets with five CDR baselines demonstrate that our method effectively mitigates the CDR non-overlapping user bias, while maintaining comparable overall accuracy in most settings. Our code is publicly available at https://github.com/WeixinChen98/VUG/tree/sc-vug.