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Capturing knowledge of user preferences: ontologies in recommender systems

Capturing knowledge of user preferences: ontologies in recommender systems
Capturing knowledge of user preferences: ontologies 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 dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.
1581133804
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Roure, David C. De
02879140-3508-4db9-a7f4-d114421375da
Shadbolt, Nigel R.
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Roure, David C. De
02879140-3508-4db9-a7f4-d114421375da
Shadbolt, Nigel R.
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7

Middleton, Stuart, Roure, David C. De and Shadbolt, Nigel R. (2001) Capturing knowledge of user preferences: ontologies in recommender systems. conference; 2001-10-01.

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 dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.

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More information

Published date: October 2001
Additional Information: Published in the proceedings of the First International Conference on Knowledge Capture, K-CAP2001 Organisation: ACM
Venue - Dates: conference; 2001-10-01, 2001-10-01
Organisations: Web & Internet Science, Electronics & Computer Science, IT Innovation

Identifiers

Local EPrints ID: 256281
URI: http://eprints.soton.ac.uk/id/eprint/256281
ISBN: 1581133804
PURE UUID: 418a8676-1167-467c-8fcf-8ad443efa4e8
ORCID for Stuart Middleton: ORCID iD orcid.org/0000-0001-8305-8176
ORCID for David C. De Roure: ORCID iD orcid.org/0000-0001-9074-3016

Catalogue record

Date deposited: 15 Apr 2002
Last modified: 15 Mar 2024 03:08

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Contributors

Author: David C. De Roure ORCID iD
Author: Nigel R. Shadbolt

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