Incorporating Relevance and Importance for Dynamic Ranking in Folksonomies

The rapidly increasing popularity of social tagging systems and growing amount of users and resources make it a difficult task to find expert users and relevant resources in folksonomies. In this paper, we propose a bipartite graph-based dynamic ranking algorithm, RicoRank (Relevance and Importance inCOrporated RANK), for improving search performance in folksonomies. We combine both the query relevance and importance effectively to generate the final ranking score. We derive the query relevance from a smoothed probabilistic generative model that demonstrates user interest and resource content. We characterize the importance with the mutual reinforcement between users and resources. We assign each mutual reinforcing relation with a weight corresponding to the coherence between the associated tags, the user interest and the resource content. Finally, we employ an iterative procedure, which incorporates well with both query relevance and importance, to simultaneously compute the ranking scores of users and resource. We conduct experiments on a dataset collected from a real-world system. Experimental results on both user expertise and resource quality ranking show a convincing performance of the proposed algorithm.