在保护隐私的前提下，解决推荐系统中的评分偏差问题。
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving
- 将偏差项显式加入本地模型损失函数，实现隐私保护下的偏差建模。
- 在三个真实数据集上，推荐准确率显著优于现有联邦推荐方法。
- 适合关注隐私安全与推荐公平性的工业级推荐系统开发者。
推荐系统旨在为用户提供个性化物品推荐，提升整体体验。传统推荐系统将所有用户数据集中存储于服务器处理，但这种中心化方式引发严重隐私担忧，因数据泄露风险日益加剧，令注重隐私的用户难以接受。为应对这一挑战，联邦学习被引入推荐系统，确保用户数据不离开本地。在中心化推荐系统中，可通过联合分析所有用户的原始交互数据有效缓解评分偏差问题；但在联邦推荐系统中，由于隐私保护限制，原始数据无法访问，导致该问题难以解决。为此，本文提出联邦偏见感知隐因子（FBALF）模型。在该模型中，训练偏差被显式纳入每个本地模型的损失函数，从而在不牺牲数据隐私的前提下有效消除评分偏差。在三个真实世界数据集上的大量实验表明，相比其他先进联邦推荐系统，FBALF实现了显著更高的推荐准确率。
原文摘要 · Abstract (English)
A recommender system (RS) aims to provide users with personalized item recommendations, enhancing their overall experience. Traditional RSs collect and process all user data on a central server. However, this centralized approach raises significant privacy concerns, as it increases the risk of data breaches and privacy leakages, which are becoming increasingly unacceptable to privacy-sensitive users. To address these privacy challenges, federated learning has been integrated into RSs, ensuring that user data remains secure. In centralized RSs, the issue of rating bias is effectively addressed by jointly analyzing all users' raw interaction data. However, this becomes a significant challenge in federated RSs, as raw data is no longer accessible due to privacy-preserving constraints. To overcome this problem, we propose a Federated Bias-Aware Latent Factor (FBALF) model. In FBALF, training bias is explicitly incorporated into every local model's loss function, allowing for the effective elimination of rating bias without compromising data privacy. Extensive experiments conducted on three real-world datasets demonstrate that FBALF achieves significantly higher recommendation accuracy compared to other state-of-the-art federated RSs.
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