Privacy-Aware and Personalised Explanation Overlays for Recommender Systems

推荐系统的隐私意识和个性化解释叠加

• 批准号: DP240101108
• 负责人: Dr Quoc Viet Hung Nguyen
• 金额: $ 25.37万
• 依托单位: Griffith University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP240101108
• 关键词: Privacy; Aware; Personalised; Explanation; Overlays

AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. The expected outcomes include graph embedding methods for capturing real-world relationships in all their messiness and complexity. The anticipated contributions include impartial and accountable recommender models that are resistant to adversarial attacks and that slow the spread of misinformation.

AI驱动的推荐系统为日常生活提供建议，但需要在法律上可以解释和解释。 该项目旨在将现有的Black-Box推荐模型转换为透明且值得信赖的决策支持系统。由此产生的工具将为建议实时提供精细的，可探索的理由，从而在推进该领域的同时增加公众的信心。预期的结果包括用于捕获现实世界中所有混乱和复杂性的现实关系的图形嵌入方法。预期的贡献包括对对抗性攻击具有抵抗力的公正和负责任的推荐模型，并减缓了错误信息的传播。