  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. In this paper, we present a tag-based
recommender system which suggests similar Web pages based on the similarity of
their tags from a Web 2.0 tagging application. The proposed approach extends
the basic similarity calculus with external factors such as tag popularity, tag
representativeness and the affinity between user and tag. In order to study and
evaluate the recommender system, we have conducted an experiment involving 38
people from 12 countries using data from Del.icio.us, a social bookmarking web
system on which users can share their personal bookmarks.
