  Recently, collaborative tagging systems have attracted more and more
attention and have been widely applied in web systems. Tags provide highly
abstracted information about personal preferences and item content, and are
therefore potential to help in improving better personalized recommendations.
In this paper, we propose a tag-based recommendation algorithm considering the
personal vocabulary and evaluate it in a real-world dataset: Del.icio.us.
Experimental results demonstrate that the usage of tag information can
significantly improve the accuracy of personalized recommendations.
