Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
Weixin Chen, Yuhan Zhao, Li Chen, Weike Pan · 2025
Cross-domain recommendation (CDR) methods predominantly leverage overlapping users to transfer knowledge from a source domain to a target domain.However, through empirical studies, we uncover a critical bias inherent in these approaches: while overlapping users experience significant enhancements in recommendation quality, non-overlapping users benefit minimally and even face performance degradation.This unfairness may erode user trust, and, consequently, negatively impact business engagement and revenue.To address this issue, we propose a novel solution that generates virtual source-domain users for non-overlapping target-domain users.Our method utilizes a dual attention mechanism to discern similarities between overlapping and non-overlapping users, thereby synthesizing realistic virtual user embeddings.We further introduce a limiter component that ensures the generated virtual users align with real-data distributions while preserving each user's unique characteristics.Notably, our method is model-agnostic and can be seamlessly integrated into any CDR model.Comprehensive experiments conducted on three public datasets with five CDR baselines demonstrate that our method effectively mitigates the CDR nonoverlapping user bias, without loss of overall accuracy.Our code is publicly available at https://github.com/WeixinChen98/VUG.