Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users

• URL: http://arxiv.org/abs/2507.17749v1
• Date: Wed, 23 Jul 2025 17:59:08 GMT
• Title: Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
• Authors: Weixin Chen, Yuhan Zhao, Li Chen, Weike Pan,
• Abstract summary: Cross-domain recommendation (CDR) methods leverage overlapping users to transfer knowledge from a source domain to a target domain.<n>We propose a novel solution that generates virtual source-domain users for non-overlapping target-domain users.<n>Our method effectively mitigates the CDR non-overlapping user bias, without loss of overall accuracy.
• Score: 13.420661387194148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 non-overlapping user bias, without loss of overall accuracy. Our code is publicly available at https://github.com/WeixinChen98/VUG.

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