Recipient of 2014 External Grants

About the Project

This project aims to develop a real-time intelligent analytics framework to infer user location, context and group dynamics by analysing ambient audio and live social network data. Its significance lies in enhancing quality of human life in urban environments via the exploration of approaches for making location-based recommendations that are informative and adaptive. This research attempts to develop a set of novel techniques aimed at:

1.   Extracting and modelling real-time social context and dynamics via a joint analysis of audio ambience and live social media information; and

2.   Supporting effective and robust recommendations base on large-scale multi-media collaborative analytics

About the Project

Smart devices have fundamentally changed the way we consume music. As emerging technologies enable effective music search on the move, intelligent recommendation systems become increasingly important. Funded by and carried out in collaboration with Microsoft Research Asia, this project aims to design, develop and evaluate a smart music recommendation engine using cloud-based media content management and statistical machine learning-based sensor data mining.

This project contributes to the building of cloud computing-based analytics infrastructure for mining user preference and developing a set of novel algorithms for recommending the right music services to the right people at the right time.