Finding hard questions by knowledge gap analysis in question answer communities

The Community Question Answer (CQA) service is a typical forum of Web2.0 in sharing knowledge among people. There are thousands of questions have been posted and solved every day. Because of the above reasons and the variant users in CQA service, the question search and ranking are the most important researches in the CQA portal. In this paper, we address the problem of detecting the question being easy or hard by means of a probability model. In addition, we observed the phenomenon called knowledge gap that is related to the habit of users and use knowledge gap diagram to illustrate how much knowledge gap in different categories. In this task, we propose an approach called knowledge-gap-based difficulty rank (KG-DRank) algorithm that combines the user-user network and the architecture of the CQA service to solve this problem. The experimental results show our approach leads to a better performance than other baseline approaches and increases the F-measure by a factor ranging from 15% to 20%.