Topical Authority Identification in Community Question Answering

Abstract

AbstractIn this paper, we address the problem of authority identification in community question answering (CQA). Most of the existing approaches attempt to identify authorities in CQA by means of link analysis techniques. However, these traditional techniques only consider the link structure while ignore the topic information about the users, giving rise to an increasing problem of topic drift. To solve the problem of topic drift, we propose a topical ranking method, which is an extension of PageRank algorithm to identify authorities in CQA. Compared to the traditional link analysis techniques, our proposed method is more effective because it measures the authority scores by taking into account both the link structure and the topic information. We conduct experiments on real world data set from Yahoo! Answers. Experimental results show that our proposed method significantly outperforms the traditional link analysis techniques and achieves the state-of-the-art performance for authority identification in CQA.Keywordsauthority identificationPageRankcommunity question answering