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- W1586647980 abstract "AbstractThe explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflects the users’ mental model and also allows the use of evolving vocabularies. However, since tags are handled in a purely syntactical way, the annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness of tag-based approaches for complex tasks. Consequently, systems called tag recommenders recently emerged, with the purpose of speeding up the so-called tag convergence, providing users with the most suitable tags for the resource to be annotated.This paper presents a tag recommender system called STaR (Social Tag Recommender), which extends the social approach presented in a previous work [14] with a content-based approach able to extract tags directly from the textual content of HTML pages.Results of experiments carried out on a large dataset gathered from Bibsonomy, show that the use of content-based techniques improves the predictive accuracy of the tag recommender.KeywordsRecommender SystemsWeb 2.0Collaborative Tagging SystemsFolksonomiesTag Recommender" @default.
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- W1586647980 date "2010-01-01" @default.
- W1586647980 modified "2023-09-25" @default.
- W1586647980 title "Combining Collaborative and Content-Based Techniques for Tag Recommendation" @default.
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- W1586647980 doi "https://doi.org/10.1007/978-3-642-15208-5_2" @default.
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