Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Agents that reduce work and information overload
Communications of the ACM
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
Using User Profiles in Intelligent Information Retrieval
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Proceedings of the 2004 ACM symposium on Applied computing
Ontology-Based User Modeling in an Augmented Audio Reality System for Museums
User Modeling and User-Adapted Interaction
A preference processing model for cooperative agents
Journal of Intelligent Information Systems
Web Intelligence and Agent Systems
Autopoiesis, the immune system, and adaptive information filtering
Natural Computing: an international journal
What Happened to Content-Based Information Filtering?
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Immune Learning in a Dynamic Information Environment
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Mutli-agent System for Personalizing Information Source Selection
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A reinforcement profile learning agent for documents filtering
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Exploiting profile modeling for web-based information systems
WISE'07 Proceedings of the 2007 international conference on Web information systems engineering
Citation-based methods for personalized search in digital libraries
WISE'07 Proceedings of the 2007 international conference on Web information systems engineering
Hybrid method for personalized search in scientific digital libraries
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
No Code Required: Giving Users Tools to Transform the Web
No Code Required: Giving Users Tools to Transform the Web
Personalised distributed information retrieval-based agents
International Journal of Intelligent Systems Technologies and Applications
A review of evolutionary and immune-inspired information filtering
Natural Computing: an international journal
Inferring word relevance from eye-movements of readers
Proceedings of the 16th international conference on Intelligent user interfaces
Using multi-attribute structures and significance term evaluation for user profile adaptation
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Interest Aware Recommendations Based on Adaptive User Profiling
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Directing exploratory search: reinforcement learning from user interactions with keywords
Proceedings of the 2013 international conference on Intelligent user interfaces
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
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This paper describes a method for learning user's interests in the Web-based personalized information filtering system called WAIR. The proposed method analyzes user's reactions to the presented documents and learns from them the profiles for the individual users. Reinforcement learning is used to adapt the term weights in the user profile so that user's preferences are best represented. In contrast to conventional relevance feedback methods which require explicit user feedbacks, our approach learns user preferences implicitly from direct observations of user behaviors during interaction. Field tests have been made which involved 7 users reading a total of 7,700 HTML documents during 4 weeks. The proposed method showed superior performance in personalized information filtering compared to the existing relevance feedback methods.