Extending a Hybrid Tag-Based Recommender System with Personalization

Frederico Durao, Peter Dolog

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25 Citationer (Scopus)

Resumé

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. This paper proposes a semantic extension for a hybrid tag-based recommender system, which suggests similar Web pages based on the similarity of their tags. The semantic extension aims at discovering tag relations which are not considered in basic syntax similarity. With the goal of generating more semantically grounded recommendations, the proposal extends a hybrid tag-based recommender system with a semantic factor, which looks for tag relations in different semantic sources. In order to evaluate the benefits acquired with the semantic extension, we have compared the new findings with results from a previous experiment involving 38 people from 12 countries using data from del.icio.us.
OriginalsprogEngelsk
TidsskriftACM Symposium on Applied Computing,
Sider (fra-til)1723-1727
DOI
StatusUdgivet - 2010

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Recommender systems
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Bibliografisk note

SAC '10 Proceedings of the 2010 ACM Symposium on Applied Computing

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Extending a Hybrid Tag-Based Recommender System with Personalization. / Durao, Frederico; Dolog, Peter.

I: ACM Symposium on Applied Computing, 2010, s. 1723-1727.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

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