@inproceedings{musto2010combining,
  abstract = {The 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. },
  address = {Berlin/Heidelberg},
  author = {Musto, Cataldo and Narducci, Fedelucio and Lops, Pasquale and de Gemmis, Marco},
  booktitle = {E-Commerce and Web Technologies},
  doi = {10.1007/978-3-642-15208-5_2},
  editor = {Buccafurri, Francesco and Semeraro, Giovanni},
  interhash = {60254c70491f83c365ee71b019d65344},
  intrahash = {bdd023e357c901c749580d038b4f2059},
  isbn = {978-3-642-15207-8},
  pages = {13--23},
  publisher = {Springer},
  series = {Lecture Notes in Business Information Processing},
  title = {Combining Collaborative and Content-Based Techniques for Tag Recommendation.},
  url = {http://dx.doi.org/10.1007/978-3-642-15208-5_2},
  volume = 61,
  year = 2010
}

@inproceedings{byde2007personalized,
  abstract = {This short paper describes a novel technique for generating personalized
	tag recommendations for users of social book- marking sites such
	as del.icio.us. Existing techniques recom- mend tags on the basis
	of their popularity among the group of all users; on the basis of
	recent use; or on the basis of simple heuristics to extract keywords
	from the url being tagged. Our method is designed to complement these
	approaches, and is based on recommending tags from urls that are
	similar to the one in question, according to two distinct similarity
	metrics, whose principal utility covers complementary cases.},
  author = {Byde, Andrew and Wan, Hui and Cayzer, Steve},
  booktitle = {Proceedings of the International Conference on Weblogs and Social
	Media},
  interhash = {38aaca7e5b9c508a5901f4109dabaa69},
  intrahash = {157846898c1c2a65c265a913ebac115a},
  month = {March},
  title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics},
  url = {http://www.icwsm.org/papers/paper47.html},
  year = 2007
}