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.