  Personalized recommender systems are confronting great challenges of
accuracy, diversification and novelty, especially when the data set is sparse
and lacks accessorial information, such as user profiles, item attributes and
explicit ratings. Collaborative tags contain rich information about
personalized preferences and item contents, and are therefore potential to help
in providing better recommendations. In this paper, we propose a recommendation
algorithm based on an integrated diffusion on user-item-tag tripartite graphs.
We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to
evaluate our algorithm. Experimental results demonstrate that the usage of tag
information can significantly improve accuracy, diversification and novelty of
recommendations.
