Adaptive Understanding of Big Data for Smart Systems

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This work will propose novel approaches for promoting better user experience and higher performance for smart information systems. Billions of people's lives are affected by smart system applications in the areas including searching, recommendation, health, gaming, etc.. When the user interacts with the system, both the user's interests and the system's contents are involving over the time. Therefore, the system's responses should be adaptive to these changes. This study will produce a novel adaptive framework to capture all the necessary information, extract knowledge, and then convert to the optimal outputs. In this process, it is very crucial to thoroughly understand the data in the system. The dynamic nature of the big data comes from the large number of items, users, and their interactions within the system. The characteristics of such big data for a smart system include the following aspects. First, the items in the system update from time to time that a large number of new items are added to the system simultaneously and the existing items are also updated or eliminated. Second, the new users of the system should be treated with proper cold start strategies for better experience. Third, the development of the system, the trend of the environment, the existing users' interests are all kept changing. The anticipated outcomes of this project are: 1) the advancement of theories of generative and predictive approaches including deep learning and reinforcement learning algorithms to understand the big data associated with the smart systems; 2) improved modelling and data mining tools to strengthen the analysis of large data sets with dynamic characteristics; 3) the development of a framework of the smart system with several key components that are able to treat items, users and the environment separately. Meanwhile, these components will communicate with each other and share the discovered knowledge. The proposed approaches will first learn the full information from the existing data in the form of individual entries as well as sequences of interactive actions, and then adapt to generate better future actions. Students will work on the project developing their expertises and ability to work with big data sets collected from real users. Knowledge will be shared in academic conferences and journals, as well as with the industrial stakeholders.