Combining Collaborative and Content-Based Techniques for Tag Recommendation

摘要

The explosion of collaborative platforms we axe recently wit-nessing, such as social networks, or video and photo sharing sites, rad-ically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for an-notating 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 re-cently emerged, with the purpose of speeding up the so-called tag con-vergence, providing users with the most suitable tags for the resource to be annotated.rnThis 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.rnResults 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.