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Belief Measure of Expertise for Experts Detection in Question Answering Communities: case study Stack Overflow

机译:问答社区中专家检测的专业知识信念度量:案例研究堆栈溢出

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Online Question Answering Communities (Q& A C) provide a valuable amount of information in several topics. The major challenge with Q& A C is the detection of the authoritative users. When manipulating real world data, we have to deal with imperfections and uncertainty that can occur. In this paper, we propose a belief measure of expertise allowing us to detect users with the highest degree of expertise based on their attributes. Experiments on a dataset from a large online Q&A Community prove that the proposed model can be used to improve the identification of most expert users.
机译:在线问答社区(Q&A C)在几个主题中提供了大量有价值的信息。问与答C的主要挑战是检测权威用户。在处理现实世界的数据时,我们必须处理可能出现的缺陷和不确定性。在本文中,我们提出了一种专业知识信念措施,使我们能够根据用户的属性来检测具有最高专业知识水平的用户。对来自大型在线问答社区的数据集进行的实验证明,该模型可用于改善大多数专家用户的身份识别。

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