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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 language | English |
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Journal | CEUR Workshop Proceedings |
Volume | 485 |
Pages (from-to) | 40-49 |
ISSN | 1613-0073 |
Publication status | Published - 2009 |
Event | International Workshop on Adaptation and Personalization for Web 2.0 - Trento, Italy Duration: 22 Jun 2009 → 24 Jun 2009 |
Conference
Conference | International Workshop on Adaptation and Personalization for Web 2.0 |
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Country/Territory | Italy |
City | Trento |
Period | 22/06/2009 → 24/06/2009 |
Keywords
- tag
- recommendation
- personalization
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KIWI: Knowledge in a Wiki
Dolog, P., Nielsen, P. A., Munk-Madsen, A., Jahn, K. & Durao, F.
01/03/2008 → 28/02/2011
Project: Research