Recommendations based on users' information-centered networks

Abstract

Danielle H. Lee is a doctoral candidate at the School of Information Sciences, University of Pittsburgh. She successfully passed her dissertation defense on November 9, 2012 and is now looking for an assistant professor job. She received her Master's degree in Information Science at Syracuse University. She worked for Samsung SDS Co. as a software engineer, Jangan College as an invited professor and Center for Dental Informatics, University of Pittsburgh as a researcher. She published several papers concentrating on knowledge sharing patterns on social media and personalized recommendations based on users' social networks in various journals and conferences, such as Journal of the American Society for Information Science and Technology; Journal of Intelligence and Information Systems; Journal of Information Processing Systems; ACM Conference on Hypertext and Hypermedia; ACM Conference on Recommender Systems; etc. She also participated in several journals and conferences (e.g. Transactions on Intelligent Systems and Technology; User Modeling and User-Adapted Interaction; Web Intelligence; User Modeling, Adaptation and Personalization; ACM Conference on Recommender Systems) as reviewers. Her research interests are personalized recommendations using users' social networks, behavioral influence and information propagation in online social networks and knowledge mining of collective intelligence.