A Personalized Tag-Based Recommendation in Social Web Systems

Frederico Durao, Peter Dolog

Research output: Contribution to journalConference article in JournalResearchpeer-review

14 Citations (Scopus)

Abstract

Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a tag-based recommender system which suggests similar Web pages based on the similarity of their tags from a Web 2.0 tagging application. The proposed approach extends the basic similarity calculus with external factors such as tag popularity, tag representativeness and the affinity between user and tag. In order to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us, a social bookmarking web system on which users can share their personal bookmarks
Original languageEnglish
JournalCEUR Workshop Proceedings
Volume485
Pages (from-to)40-49
ISSN1613-0073
Publication statusPublished - 2009
EventInternational Workshop on Adaptation and Personalization for Web 2.0 - Trento, Italy
Duration: 22 Jun 200924 Jun 2009

Conference

ConferenceInternational Workshop on Adaptation and Personalization for Web 2.0
CountryItaly
CityTrento
Period22/06/200924/06/2009

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Recommender systems
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Experiments

Keywords

  • tag
  • recommendation
  • personalization

Cite this

@inproceedings{22ba11e0ebd311deb63d000ea68e967b,
title = "A Personalized Tag-Based Recommendation in Social Web Systems",
abstract = "Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a tag-based recommender system which suggests similar Web pages based on the similarity of their tags from a Web 2.0 tagging application. The proposed approach extends the basic similarity calculus with external factors such as tag popularity, tag representativeness and the affinity between user and tag. In order to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us, a social bookmarking web system on which users can share their personal bookmarks",
keywords = "tag, recommendation, personalization",
author = "Frederico Durao and Peter Dolog",
year = "2009",
language = "English",
volume = "485",
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journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "CEUR Workshop Proceedings",

}

A Personalized Tag-Based Recommendation in Social Web Systems. / Durao, Frederico; Dolog, Peter.

In: CEUR Workshop Proceedings, Vol. 485, 2009, p. 40-49.

Research output: Contribution to journalConference article in JournalResearchpeer-review

TY - GEN

T1 - A Personalized Tag-Based Recommendation in Social Web Systems

AU - Durao, Frederico

AU - Dolog, Peter

PY - 2009

Y1 - 2009

N2 - Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a tag-based recommender system which suggests similar Web pages based on the similarity of their tags from a Web 2.0 tagging application. The proposed approach extends the basic similarity calculus with external factors such as tag popularity, tag representativeness and the affinity between user and tag. In order to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us, a social bookmarking web system on which users can share their personal bookmarks

AB - Tagging activity has been recently identified as a potential source of knowledge about personal interests, preferences, goals, and other attributes known from user models. Tags themselves can be therefore used for finding personalized recommendations of items. In this paper, we present a tag-based recommender system which suggests similar Web pages based on the similarity of their tags from a Web 2.0 tagging application. The proposed approach extends the basic similarity calculus with external factors such as tag popularity, tag representativeness and the affinity between user and tag. In order to study and evaluate the recommender system, we have conducted an experiment involving 38 people from 12 countries using data from Del.icio.us, a social bookmarking web system on which users can share their personal bookmarks

KW - tag

KW - recommendation

KW - personalization

M3 - Conference article in Journal

VL - 485

SP - 40

EP - 49

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

ER -