基于知识缺口分析的问答社区问题难度评价

Chih-Lu Lin, Ying-Liang Chen, Hung-Yu kao
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引用次数: 10

摘要

社区问答(CQA)服务是一个典型的Web 2.0论坛,可以在人们之间共享知识。每天都有成千上万的问题被发布和解决。由于CQA服务的用户种类繁多,因此问题搜索和排名是CQA门户网站中最重要的研究课题。在这项研究中,我们通过概率模型解决了识别问题的难易问题。此外,我们观察到与用户习惯相关的知识差距现象,并使用知识差距图来说明不同类别中存在多少知识差距。为此,我们提出了一种基于知识缺口的难度等级(kg - drink)算法,该算法将用户-用户网络和CQA服务的体系结构相结合来发现难题。我们使用f-measure、AUC、MAP、NDCG、precision@Top5和一致性分析对实验结果进行评价。我们的结果表明,我们的方法在所有评估指标上比其他基线方法产生更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Question difficulty evaluation by knowledge gap analysis in Question Answer communities
The Community Question Answer (CQA) service is a typical forum of Web 2.0 that shares knowledge among people. There are thousands of questions that are posted and solved every day. Because of the various users of the CQA service, question search and ranking are the most important topics of research in the CQA portal. In this study, we addressed the problem of identifying questions as being hard or easy by means of a probability model. In addition, we observed the phenomenon called knowledge gap that is related to the habit of users and used a knowledge gap diagram to illustrate how much of a knowledge gap exists in different categories. To this end, we proposed an approach called the knowledge-gap-based difficulty rank (KG-DRank) algorithm, which combines the user-user network and the architecture of the CQA service to find hard questions. We used f-measure, AUC, MAP, NDCG, precision@Top5 and concordance analysis to evaluate the experimental results. Our results show that our approach leads to better performance than other baseline approaches across all evaluation metrics.
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