发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究个性化推荐系统中隐私保护问题，提出结合联邦学习、差分隐私等的框架，在合成零售数据集上实验，评估推荐质量指标，结果显示该框架在适度隐私预算下能保持竞争力，为隐私保护推荐系统提供实用框架。
AI 中文摘要
个性化推荐系统是现代电子商务和零售平台的核心，但依赖集中存储详细用户交互数据带来隐私和监管挑战。随着GDPR等法规要求提高，组织须开发保护用户隐私且不大幅降低推荐质量的系统。本文提出并评估了一个结合联邦学习、差分隐私、群组级建模和隐私感知智能代理的隐私保护推荐框架。该框架使原始用户数据分散，同时引入有数学边界的噪声到模型更新中。在模拟客户点击流和购买行为的合成零售数据集上进行实验，使用点击率、精准率、召回率和归一化折损累计增益等指标评估推荐质量。结果表明，该框架在适度隐私预算下保持有竞争力的推荐质量，为部署平衡个性化、合规性和业务目标的隐私保护推荐系统提供了实用框架。
英文摘要
Personalized recommendation systems are central to modern e-commerce and retail platforms, but they typically rely on centralized storage of detailed user interaction data, creating significant privacy and regulatory challenges. With increasing requirements from regulations such as GDPR, CCPA, and CPRA, organizations must develop recommendation systems that preserve user privacy without substantially degrading recommendation quality. This work presents and evaluates a privacy-preserving recommendation framework that combines federated learning, differential privacy, cohort-level modeling, and privacy-aware intelligent agents. The framework keeps raw user data decentralized while introducing mathematically bounded noise to model updates. Experiments were conducted on synthetic retail datasets that emulate customer clickstream and purchase behavior. Recommendation quality was evaluated using Click-Through Rate (CTR), Precision@K, Recall@K, and Normalized Discounted Cumulative Gain (NDCG@K) across multiple differential privacy budgets. We evaluate matrix factorization, neural collaborative filtering, and GRU4Rec under varying privacy constraints and analyze the trade-off between privacy and utility. An interactive Streamlit dashboard was developed to visualize recommendation performance, ranking stability, privacy-utility trade-offs, and fairness metrics. Results show that the proposed framework maintains competitive recommendation quality at moderate privacy budgets (approximately $ε\approx 5$), demonstrating that strong privacy guarantees can be achieved with limited impact on recommendation effectiveness. This work provides a practical framework for deploying privacy-preserving recommendation systems that balance personalization, regulatory compliance, and business objectives, offering a scalable approach for next-generation AI-driven retail platforms.
