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In this paper, we address the problem of expert finding in community question answering (CQA). Most of the existing approaches attempt to find experts in CQA by means of link analysis techniques. However, these traditional techniques only consider the link structure while ignore the topical similarity among users (askers and answerers) and user expertise and user reputation. In this study, we propose a topic-sensitive probabilistic model, which is an extension of PageRank algorithm to finddoi:10.1145/2396761.2398493 dblp:conf/cikm/ZhouLLZ12 fatcat:qyy5v2oqo5bedh6u4jiiwanq3i