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Multiple interests of users in collaborative tagging systems

Multiple interests of users in collaborative tagging systems
Multiple interests of users in collaborative tagging systems
Performance of recommender systems depends on whether the user profiles contain accurate information about the interests of the users, and this ill turn relies on whether enough information about their interests can be collected. Collaborative tagging systems allow users to use their own words to describe their favourite resources, resulting in some user-generated categorisation schemes commonly known as folksonomies. Folksonomics thus contain rich information about the interests of the users, which can be used to support various recommender systems. Our analysis of the folksonomy in Delicious reveals that the interests of a single user can be very diverse. Traditional methods for representing interests of users are usually not able to reflect Such diversity. We propose a method to construct user profiles of multiple interests from folksonomies based oil a network clustering technique. Our evaluation shows that the proposed method is able to generate user profiles which reflect the diversity Of user interests and can be used as a basis of providing more focused recommendation to the users.
255-274
Springer Berlin
Yeung, Ching-man Au
21e57236-80e8-474d-9f31-0c9c8ec67bf9
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
King, Irwin
Baeza-Yates, Ricardo
Yeung, Ching-man Au
21e57236-80e8-474d-9f31-0c9c8ec67bf9
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
King, Irwin
Baeza-Yates, Ricardo

Yeung, Ching-man Au, Gibbins, Nicholas and Shadbolt, Nigel (2009) Multiple interests of users in collaborative tagging systems. In, King, Irwin and Baeza-Yates, Ricardo (eds.) Weaving Services and People on the World Wide Web. (Weaving Services and People on the World Wide Web) Springer Berlin, pp. 255-274.

Record type: Book Section

Abstract

Performance of recommender systems depends on whether the user profiles contain accurate information about the interests of the users, and this ill turn relies on whether enough information about their interests can be collected. Collaborative tagging systems allow users to use their own words to describe their favourite resources, resulting in some user-generated categorisation schemes commonly known as folksonomies. Folksonomics thus contain rich information about the interests of the users, which can be used to support various recommender systems. Our analysis of the folksonomy in Delicious reveals that the interests of a single user can be very diverse. Traditional methods for representing interests of users are usually not able to reflect Such diversity. We propose a method to construct user profiles of multiple interests from folksonomies based oil a network clustering technique. Our evaluation shows that the proposed method is able to generate user profiles which reflect the diversity Of user interests and can be used as a basis of providing more focused recommendation to the users.

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More information

e-pub ahead of print date: 1 January 2009
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 270544
URI: http://eprints.soton.ac.uk/id/eprint/270544
PURE UUID: 5288ff11-8b9e-4fec-9953-c5dc3134adc9
ORCID for Nicholas Gibbins: ORCID iD orcid.org/0000-0002-6140-9956

Catalogue record

Date deposited: 21 Apr 2010 07:46
Last modified: 22 Mar 2024 02:35

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Contributors

Author: Ching-man Au Yeung
Author: Nicholas Gibbins ORCID iD
Author: Nigel Shadbolt
Editor: Irwin King
Editor: Ricardo Baeza-Yates

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