Exploiting Content Quality and Question Difficulty in CQA Reputation Systems

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• 关键词：Community Question Answering ; User reputation ; Expertise estimation
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9564
• 期：1
• 页码：68-81
• 全文大小：263 KB
• 参考文献：1.Bosu, A., Corley, C.S., Heaton, D., Chatterji, D., Carver, J.C., Kraft, N.A.: Building reputation in stackoverflow: an empirical investigation. In: Proceedings of the 10th Working Conference on Mining Software Repositories, MSR 2013, pp. 89–92. IEEE Press, Piscataway (2013)
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• 作者单位：Adrian Huna (17)
  Ivan Srba (17)
  Maria Bielikova (17)
  
  17. Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Ilkovicova 2, 842 16, Bratislava, Slovakia
  
• 丛书名：Advances in Network Science
• ISBN：978-3-319-28361-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Community Question Answering (CQA) systems (e.g. StackOverflow) have gained popularity in the last years. With the increasing community size and amount of user generated content, a task of expert identification arose. To tackle this problem, various reputation mechanisms exist, however, they estimate user reputation especially according to overall user activity, while the quality of contributions is considered only secondary. As the result, reputation usually does not reflect the real value of users’ contributions and, moreover, some users (so called reputation collectors) purposefully abuse reputation systems to achieve a high reputation score. We propose a novel reputation mechanism that focuses primarily on the quality and difficulty of users’ contributions. Calculated reputation was compared with four baseline methods including the reputation schema employed in Stack Exchange platform. The experimental results showed a higher precision achieved by our approach, and confirmed an important role of contribution quality and difficulty in estimation of user reputation.