Abstract
Techniques for both exploratory and known item search tend to direct only to more specific subtopics or individual documents, as opposed to allowing directing the exploration of the information space. We present SciNet, an interactive information retrieval system that combines Reinforcement Learning techniques along with a novel user interface design to allow active engagement of users in directing the search. Users can directly manipulate document features (keywords) to indicate their interests and Reinforcement Learning is used to model the user by allowing the system to trade off between exploration and exploitation. This gives users the opportunity to more effectively direct their search.
Original language | English |
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Title of host publication | IUI '13 Companion Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion |
Publisher | ACM |
Pages | 61-62 |
Number of pages | 2 |
ISBN (Print) | 978-1-4503-1966-9 |
DOIs | |
Publication status | Published - 2013 |