Capturing knowledge of user preferences with recommender systems
Capturing knowledge of user preferences with recommender systems
Capturing user preferences is a problematic task. Simply asking the users what they want is too intrusive and prone to error, yet monitoring behaviour unobtrusively and finding meaningful patterns is both difficult and computationally time consuming. Capturing accurate user preferences is, however, an essential task if the information systems of tomorrow are to respond dynamically to the changing needs of their users. This thesis tests the hypothesis that using an ontology to represent user profiles offers advantages over traditional profile representations in the context of recommender systems. A novel ontology-based approach to recommendation is applied to a real world problem and empirically evaluated. Synergy between recommender systems and ontologies is then explored to help overcome both the recommender system cold-start problem and the ontology interest-acquisition problem. Finally, the visualization of profiles in ontological terms is examined in a real world situation and empirically evaluated.
recommender systems, ontologies, ontology, user profiling, user modelling, machine learning
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
May 2003
Middleton, Stuart
404b62ba-d77e-476b-9775-32645b04473f
Middleton, Stuart
(2003)
Capturing knowledge of user preferences with recommender systems.
University of Southampton, ECS, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
Capturing user preferences is a problematic task. Simply asking the users what they want is too intrusive and prone to error, yet monitoring behaviour unobtrusively and finding meaningful patterns is both difficult and computationally time consuming. Capturing accurate user preferences is, however, an essential task if the information systems of tomorrow are to respond dynamically to the changing needs of their users. This thesis tests the hypothesis that using an ontology to represent user profiles offers advantages over traditional profile representations in the context of recommender systems. A novel ontology-based approach to recommendation is applied to a real world problem and empirically evaluated. Synergy between recommender systems and ontologies is then explored to help overcome both the recommender system cold-start problem and the ontology interest-acquisition problem. Finally, the visualization of profiles in ontological terms is examined in a real world situation and empirically evaluated.
Text
Thesis-final-lowres.pdf
- Other
Other
Thesis-final-lowres.ps
- Other
More information
Published date: May 2003
Keywords:
recommender systems, ontologies, ontology, user profiling, user modelling, machine learning
Organisations:
University of Southampton, Electronics & Computer Science, IT Innovation
Identifiers
Local EPrints ID: 257857
URI: http://eprints.soton.ac.uk/id/eprint/257857
PURE UUID: 4f46b55d-8bf0-416e-81d1-8f368ae60b78
Catalogue record
Date deposited: 25 Jun 2003
Last modified: 15 Mar 2024 03:08
Export record
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
