Adaptive Understanding of Big Data for Smart Systems

这项工作将为促进智能信息系统更好的用户体验和更高的性能提出新的方法。包括搜索、推荐、健康、游戏等领域的智能系统应用影响着数十亿人的生活。当用户与系统互动时，随着时间的推移，用户的兴趣和系统的内容都会涉及到。因此，系统的反应应该适应这些变化。这项研究将产生一个新的自适应框架，以捕获所有必要的信息，提取知识，然后转换为最优输出。在这个过程中，彻底了解系统中的数据是非常关键的。大数据的动态性来自系统中的大量项目、用户及其交互。这样的大数据对于智能系统的特点包括以下几个方面。首先，系统中的条目不定期更新，大量新条目同时添加到系统中，现有条目也被更新或删除。其次，应以适当的冷启动策略对待该系统的新用户，以获得更好的体验。第三，系统的发展、环境的趋势、现有用户的兴趣都在不断变化。该项目的预期成果是：1)生成性和预测性方法理论的进步，包括深度学习和强化学习算法，以理解与智能系统相关的大数据；2)改进的建模和数据挖掘工具，以加强对具有动态特征的大数据集的分析；3)开发智能系统的框架，包括几个能够分别对待项目、用户和环境的关键组件。同时，这些组件将相互通信，共享发现的知识。所提出的方法将首先以单个条目和交互动作序列的形式从现有数据中学习完整的信息，然后进行调整以生成更好的未来动作。学生们将致力于这个项目，发展他们的专业知识和使用从真实用户那里收集的大数据集的能力。知识将在学术会议和期刊上分享，以及与行业利益攸关方分享。

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.