Capturing Knowledge of User Preferences: ontologies on recommender systems

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dc.creatorMiddleton, S. E.
dc.creatorDe Roure, D. C.
dc.creatorShadbolt, N. R.
dc.date2002-03-08
dc.date.accessioned2026-07-07T03:18:12Z
dc.date.available2026-07-07T03:18:12Z
dc.descriptionTools 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.
dc.descriptionFirst international conference on Knowledge Capture 2001, 8 pages
dc.identifierhttps://arxiv.org/abs/cs/0203011
dc.identifierhttp://arxiv.org/abs/cs/0203011
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31018
dc.subjectMachine Learning
dc.subjectMultiagent Systems
dc.subjectI.2.6;I.2.11
dc.titleCapturing Knowledge of User Preferences: ontologies on recommender systems
dc.typetext

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