Privacy-Aware and Personalised Explanation Overlays for Recommender Systems

人工智能推荐系统为日常生活提供建议，但它们需要具有法律可解释性和可解释性。本项目旨在将现有的黑盒推荐模型转化为透明可信的决策支持系统。由此产生的工具将实时为建议提供精细的、可探索的基本原理，在推进该领域的同时创造更大的公众信心。预期的结果包括图嵌入方法，用于捕获现实世界中所有混乱和复杂的关系。预期的贡献包括公正和负责任的推荐模型，这些模型可以抵抗对抗性攻击并减缓错误信息的传播。

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.