Robust Preference Inference from Spatial-Temporal Interaction Networks

该项目旨在开发创新技术，从标记较少、稀疏和嘈杂的交互数据中有效地管理用户偏好配置文件。一个统一的新颖学习框架以及一套数据分析技术有望从这个项目中开发出来，这将提供一种非侵入式的方式，通过发现人类显性和隐性兴趣域来对用户偏好分析进行预测分析。该应用程序的预期结果不仅将保持澳大利亚在这一前沿研究领域的领先地位，而且还将支持许多保护澳大利亚人民和经济的重要应用，如网络安全，医疗保健和电子商务。

This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human explicit and implicit interest domains. The expected results of this application will not only maintain Australia's leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as cyber security, healthcare, and e-Commerce.