This talk was given by Dr. Qiang Yang of Hong Kong University of Science and Technology as part of the IGERT Distinguished Speaker Series and Allred Distinguished Lecture in Artificial Intelligence on October 10, 2013.
The ever-growing social media and mobile computing platforms provide invaluable sources of information for modeling the behavior of users. High-quality user models enable superior services and functions for end users. In this talk, I will present several examples of user modeling based on social networks and social media. I will first describe our research in modeling users’ information preferences on Microblogs using a novel user message model. I will then discuss our work on extracting users’ daily activities, such as dining and shopping, that inherently reflect their habits, intents and preferences. I explain our novel transfer learning solution via a collaborative boosting framework comprising a text-to-activity classifier for socially connected users. I will also discuss our work on modeling the user behavior in mobile computing environments for building context-aware and adaptive information agents.
(Source: WSU Smart Environments)