Robust meta learning for risk-aware recommender systems

用于风险感知推荐系统的鲁棒元学习

• 批准号: DP220102635
• 负责人: A/Prof Guangquan Zhang
• 金额: $ 35.53万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-10-03 至 2025-10-02
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220102635
• 关键词: Robust; meta; learning; risk; aware

Recommender systems are the core of many online services but they are highly vulnerable to risks like shilling attacks, privacy leaks, and unexpected change. This project aims to develop new adversarial Bayesian-based, privacy-preserved and self-adaptive fuzzy meta learning methods and meta recommender systems that are robust to these risky, uncertain and dynamic environments. The anticipated outcomes should significantly improve the reliability of recommender systems with particular benefits for online personalised service systems, e.g., e-government, e-business and e-Learning. The outcomes will also advance machine learning knowledge with a new robust meta learning schema for general data analytics and applications.

推荐系统是许多在线服务的核心，但它们非常容易受到先令攻击、隐私泄露和意外变化等风险的影响。该项目旨在开发新的基于对抗性贝叶斯、隐私保护和自适应的模糊元学习方法和元推荐系统，这些方法和元推荐系统对这些风险、不确定和动态环境具有鲁棒性。预期结果应显着提高推荐系统的可靠性，特别有利于在线个性化服务系统，例如电子政务、电子商务和电子学习。结果还将通过用于一般数据分析和应用的新的强大元学习模式来推进机器学习知识。