About the Project
The general aim of this project is to develop scalable and effective user profiling approaches to discover new knowledge about users’ individual interests, preferences, sentiment and emotional status, and information needs from large scale social and pervasive data, for the purpose of facilitating recommender systems to provide better services for users. To better capture the unique distinctive features of social and pervasive data, this project will adopt advanced machine learning techniques such as reinforcement learning, deep learning, and semi-supervised machine learning methods to develop novel user feature learning models. Its outcomes will alleviate the major difficulty--the lacking of accurate user profiles--of many personalisation in many domains such as e-commerce, e-health, and e-learning. This project will contribute to the discovery of new knowledge about human users and the social world, and the new solutions to make better usage and processing of big data.
Eligibility Requirement: First degree in computer science, physics, engineering, and mathematics with 2:1 or above. MSc degree in the relevant subject areas is desired. Good math knowledge and good programming skills are required
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