Instance-Based Learning Algorithms
Machine Learning
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Capturing knowledge of user preferences: ontologies in recommender systems
Proceedings of the 1st international conference on Knowledge capture
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Time spent on a web page is sufficient to infer a user's interest
IMSA'07 IASTED European Conference on Proceedings of the IASTED European Conference: internet and multimedia systems and applications
Web search personalization with ontological user profiles
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Proceedings of the 2008 ACM symposium on Applied computing
A knowledge retrieval model using ontology mining and user profiling
Integrated Computer-Aided Engineering
An ontology-based approach for providing multimedia personalised recommendations
International Journal of Web and Grid Services
MUADDIB: A distributed recommender system supporting device adaptivity
ACM Transactions on Information Systems (TOIS)
A fully automated recommender system using collaborative filters
CIIT '07 The Sixth IASTED International Conference on Communications, Internet, and Information Technology
Creating User Profiles Using Wikipedia
ER '09 Proceedings of the 28th International Conference on Conceptual Modeling
Combining Various Methods of Automated User Decision and Preferences Modelling
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Learning User Preferences for 2CP-Regression for a Recommender System
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
Time spent on a web page is sufficient to infer a user's interest
EurolMSA '07 Proceedings of the Third IASTED European Conference on Internet and Multimedia Systems and Applications
Ontological technologies for user modelling
International Journal of Metadata, Semantics and Ontologies
User profiles for personalized information access
The adaptive web
Representing context in web search with ontological user profiles
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Using ontological modeling in a context-aware summarization system to adapt text for mobile devices
Active conceptual modeling of learning
Keyword clustering for user interest profiling refinement within paper recommender systems
Journal of Systems and Software
Personalised search-a hybrid approach for web information retrieval and its evaluation
International Journal of Knowledge and Web Intelligence
UP-DRES: user profiling for a dynamic REcommendation system
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
SMARTMUSEUM: A mobile recommender system for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
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Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a diverse and dynamic environment. Recommender systems help where explicit search queries are not available or are difficult to formulate, learning the type of thing users like over a period of time.We explore an ontological approach to user profiling in the context of a recommender system. Building on previous work involving ontological profile inference and the use of external ontologies to overcome the cold-start problem, we explore the idea of profile visualization to capture further knowledge about user interests. Our system, called Foxtrot, examines the problem of recommending on-line research papers to academic researchers. Both our ontological approach to user profiling and our visualization of user profiles are novel ideas to recommender systems. A year long experiment is conducted with over 200 staff and students at the University of Southampton. The effectiveness of visualizing profiles and eliciting profile feedback is measured, as is the overall effectiveness of the recommender system.