Tag recommendation based on user interest lattice matching

Social tagging is becoming more and more popular in various Web 2.0 applications nowadays. It is important for many web-sites with tagging capabilities like “delicious” or “flickr”. These social tagging systems usually include tag recommendation mechanism which assist users in tagging process by suggesting relevant tags to them, where tag recommendation is the task of predicting a personalized list of tags for a user given an item. In this paper, we propose an approach for tag recommendation based on users´ interest lattice matching (UILM). UILM constructs the users´ interest lattice according to users´ interest context extracted from tagging data. Lattice Matching is then proposed and applied to obtain the users that are similar to the current user. Finally, we show the feasibility and efficiency of our approach through experiments.