  Social tagging, as a novel approach to information organization and
discovery, has been widely adopted in many Web2.0 applications. The tags
provide a new type of information that can be exploited by recommender systems.
Nevertheless, the sparsity of ternary <user, tag, item> interaction data limits
the performance of tag-based collaborative filtering. This paper proposes a
random-walk-based algorithm to deal with the sparsity problem in social tagging
data, which captures the potential transitive associations between users and
items through their interaction with tags. In particular, two smoothing
strategies are presented from both the user-centric and item-centric
perspectives. Experiments on real-world data sets empirically demonstrate the
efficacy of the proposed algorithm.
