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Capturing Interest Through Inference and Visualization: Ontological User Profiling in Recommender Systems

Capturing Interest Through Inference and Visualization: Ontological User Profiling in Recommender Systems
Capturing Interest Through Inference and Visualization: Ontological User Profiling in Recommender Systems
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.
Knowledge capture, Machine learning, Ontology, Profile visualization, Recommender systems, User profiling, User modelling
Middleton, Stuart E.
404b62ba-d77e-476b-9775-32645b04473f
Shadbolt, N.R.
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
De Roure, D.C.
02879140-3508-4db9-a7f4-d114421375da
Middleton, Stuart E.
404b62ba-d77e-476b-9775-32645b04473f
Shadbolt, N.R.
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
De Roure, D.C.
02879140-3508-4db9-a7f4-d114421375da

Middleton, Stuart E., Shadbolt, N.R. and De Roure, D.C. (2003) Capturing Interest Through Inference and Visualization: Ontological User Profiling in Recommender Systems. K-CAP2003, United States.

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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More information

Published date: 2003
Additional Information: Event Dates: October
Venue - Dates: K-CAP2003, United States, 2003-10-01
Keywords: Knowledge capture, Machine learning, Ontology, Profile visualization, Recommender systems, User profiling, User modelling
Organisations: Web & Internet Science, Electronics & Computer Science, IT Innovation

Identifiers

Local EPrints ID: 258932
URI: http://eprints.soton.ac.uk/id/eprint/258932
PURE UUID: 48ef7da2-bdd3-4d1a-8a37-0f8814a8dbbd
ORCID for Stuart E. Middleton: ORCID iD orcid.org/0000-0001-8305-8176
ORCID for D.C. De Roure: ORCID iD orcid.org/0000-0001-9074-3016

Catalogue record

Date deposited: 05 Mar 2004
Last modified: 10 Dec 2019 01:48

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Contributors

Author: N.R. Shadbolt
Author: D.C. De Roure ORCID iD

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