HeterRank

Abstract

A social tagging system provides users an effective way to collaboratively annotate and organize items with their own tags. A social tagging system contains heterogenous information like users' tagging behaviors, social networks, tag semantics and item profiles. All the heterogenous information helps alleviate the cold start problem due to data sparsity. In this paper, we model a social tagging system as a multi-type graph and propose a graph-based ranking algorithm called HeterRank for tag recommendation. Experimental results on three publicly available datasets, i.e., CiteULike, Last.fm and Delicious prove the effectiveness of HeterRank for tag recommendation with heterogenous information.