Making sense of digital information is a growing problem in almost every domain, ranging from scientists needing to stay current with new research, to companies aiming to provide the best service for their customers. What is common to such "Big Data" problems is not only the scale of the data, but also the complexity of the human processes that continuously interact with digital environments and generate new data.
Focusing on learning problems that arise in retrieval and recommender systems, I will show how we can develop principled approaches that explicitly model the process of continuously learning with humans in the loop while improving system utility. As one example, I will present the linear submodular bandits problem, which jointly addresses the challenges of selecting optimally diversified recommendations and balancing the exploration/exploitation tradeoff when personalizing via user feedback. More generally, I will show how to integrate the collection of training data with the user's use of the system in a variety of applications, ranging from long-term optimization of personalized recommender systems to disambiguation within a single search session.
Yisong Yue is a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. His research interests lie primarily in machine learning approaches to structured prediction and interactive systems, with an application focus in developing new approaches for information retrieval and access. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. He is the author of the SVM-map software package for optimizing mean average precision using support vector machines. His current research focuses on machine learning approaches to diversified retrieval and interactive information systems.