A unified learning paradigm for large-scale personalized information management

Statistical-learning approaches such as unsupervised learning, supervised learning, active learning, and reinforcement learning have generally been separately studied and applied to solve application problems. In this paper, we provide an overview of our newly proposed unified learning paradigm (ULP), which combines these approaches into one synergistic framework. We outline the architecture and the algorithm of ULP, and explain benefits of employing this unified learning paradigm on personalizing information management.