Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Advantages of query biased summaries in information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Constructing, organizing, and visualizing collections of topically related Web resources
ACM Transactions on Computer-Human Interaction (TOCHI)
A reinforcement learning agent for personalized information filtering
Proceedings of the 5th international conference on Intelligent user interfaces
Searching the Web: the public and their queries
Journal of the American Society for Information Science and Technology
Exploring Versus Exploiting when Learning User Models for Text Recommendation
User Modeling and User-Adapted Interaction
Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interactive Visualization of Multiple Query Results
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
The perfect search engine is not enough: a study of orienteering behavior in directed search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Findex: search result categories help users when document ranking fails
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploratory search: from finding to understanding
Communications of the ACM - Supporting exploratory search
Elicitation of term relevance feedback: an investigation of term source and context
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Interactively optimizing information retrieval systems as a dueling bandits problem
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Predicting short-term interests using activity-based search context
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Apolo: making sense of large network data by combining rich user interaction and machine learning
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Citeology: visualizing paper genealogy
CHI '12 Extended Abstracts on Human Factors in Computing Systems
SciNet: a system for browsing scientific literature through keyword manipulation
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Directing exploratory search with interactive intent modeling
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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 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 nearer, further and following a direction. A task-based user study conducted with 20 participants comparing our system to a traditional query-based baseline indicates that our system significantly improves the effectiveness of information retrieval by providing access to more relevant and novel information without having to spend more time acquiring the information.