Researchers must navigate big data. Current scientific knowledge includes 50 million published articles. How can a system help a researcher find relevant documents in their field? We introduce SciNet, an interactive search system that anticipates the user’s search intents by estimating them from the user’s interaction with the interface. The estimated intents are visualized on an intent radar, a radial layout that organizes potential intents as directions in the information space. The system assists users to direct their search by allowing feedback to be targeted on keywords representing the potential intents. Users can provide feedback by moving the keywords on the intent radar. The system then learns and visualizes improved estimates and corresponding documents. The resulting user models are explicit open user models curated by the user during the interactive information seeking. SciNet has been shown to significantly improve users’ task performance and the quality of retrieved information without compromising task execution time. We also show how user models learned in SciNet can be used to help cold-start recommendation in another system, the CoMeT talk management system, by cross-system user model transfer across the systems.
Bio: Jaakko Peltonen is an Associate Professor of statistics (data analysis) at the School of Information Sciences, University of Tampere, Finland; he is also currently an academy research fellow at Aalto University, Finland, where he is a PI of the Probabilistic Machine Learning research group. He is an associate editor of Neural Processing Letters and an editorial board member of Heliyon. He has served in organizing committees of seven international conferences and one international summer school, has served in program committees of 28 international conferences/workshops and has performed referee duties for numerous international journals and conferences. He has 74 publications and has 730 citations so far (h-index 14). He is an expert in statistical machine learning methods for exploratory data analysis, visualization of data, and learning from multiple sources.