  In this paper, based on the user-tag-object tripartite graphs, we propose a
recommendation algorithm, which considers social tags as an important role for
information retrieval. Besides its low cost of computational time, the
experiment results of two real-world data sets, \emph{Del.icio.us} and
\emph{MovieLens}, show it can enhance the algorithmic accuracy and diversity.
Especially, it can obtain more personalized recommendation results when users
have diverse topics of tags. In addition, the numerical results on the
dependence of algorithmic accuracy indicates that the proposed algorithm is
particularly effective for small degree objects, which reminds us of the
well-known \emph{cold-start} problem in recommender systems. Further empirical
study shows that the proposed algorithm can significantly solve this problem in
social tagging systems with heterogeneous object degree distributions.
