QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
复制标题
DOI：
10.1609/icwsm.v12i1.15015
复制
发表时间：
2018-03
影响因子：
5.3
通讯作者：
Jiankai Sun-;Sobhan Moosavi;R. Ramnath;S. Parthasarathy
中科院分区：
文献类型：
--
作者：
Jiankai Sun-;Sobhan Moosavi;R. Ramnath;S. Parthasarathy
在本文中，我们提出了一个用于社区问答网站（CQA）（例如 Yahoo!）中的问题难度和专业知识估计（QDEE）的框架。 Answers 和 Stack Overflow，它解决了众包中的一个基本挑战：如何适当地向具有适当专业知识的用户路由和分配问题。这个问题领域一直是许多研究的主题，包括与语言无关的解决方案和语言意识的解决方案。我们提出了一个与语言无关的关键见解：随着时间的推移，用户获得专业知识，因此倾向于提出并回答更困难的问题。我们在流行的竞争（定向）图模型中使用这种洞察力，通过识别所述模型中的关键层次结构来估计问题难度和用户专业知识。这里的一个重要而新颖的贡献是将“社会痛苦”应用于这个问题领域。新发布的问题（冷启动问题）的难度级别是通过使用我们的 QDEE 框架和附加文本特征来估计的。我们还提出了一种模型，根据问题的难度级别和用户的专业知识将新发布的问题路由给适当的用户。对真实世界的 CQA（例如 Yahoo!）进行了广泛的实验Answers 和 Stack Overflow 数据证明了我们的方法相对于当代最先进模型的效率有所提高。
In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of ``social agony'' to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models.