Finding Experts in Tag Based Knowledge Sharing Communities

With the rapid development of online Knowledge Sharing Communities (KSCs), the problem of finding experts becomes increasingly important for knowledge propagation and putting crowd wisdom to work. A recent development trend of KSCs is to allow users to add text tags for annotating their posts, which are more accurate than traditional category information. However, how to leverage these user-generated tags for finding experts is still under-explored. To this end, in this paper, we develop a novel approach for finding experts in tag based KSCs by leveraging tag context and the semantic relationship between tags. Specifically, the extracted prior knowledge and user profiles are first used for enriching the query tags to infer tag context, which represents the user’s latent information needs. Then, a topic model based approach is applied for capturing the semantic relationship between tags and then taking advantage of them for ranking user authority. We evaluate the proposed framework for expert finding on a large-scale real-world data set collected from a tag based Chinese commercial Q&A web site. Experimental results clearly show that the proposed method outperforms several baseline methods with a significant margin.