Robust Preference Inference from Spatial-Temporal Interaction Networks

该项目旨在开发创新技术，以有效和高效地管理来自标签较少，稀疏和嘈杂的交互数据的用户偏好配置文件。一个统一的新的学习框架沿着一套数据分析技术，预计将从这个项目，这将提供一个非侵入性的方式进行预测分析用户偏好分析，通过发现人类显式和隐式的兴趣领域。这一应用的预期成果不仅将保持澳大利亚在这一前沿研究领域的领导地位，还将支持许多保障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.