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Erschienen in: Journal of Intelligent Information Systems 1/2013

01.02.2013

Content-based and collaborative techniques for tag recommendation: an empirical evaluation

verfasst von: Pasquale Lops, Marco de Gemmis, Giovanni Semeraro, Cataldo Musto, Fedelucio Narducci

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 1/2013

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Abstract

The rapid growth of the so-called Web 2.0 has changed the surfers’ behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users’ mental model; secondly, the absence of a controlled vocabulary reduces the users’ learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results.

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Metadaten
Titel
Content-based and collaborative techniques for tag recommendation: an empirical evaluation
verfasst von
Pasquale Lops
Marco de Gemmis
Giovanni Semeraro
Cataldo Musto
Fedelucio Narducci
Publikationsdatum
01.02.2013
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 1/2013
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-012-0215-6

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