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数据分析与知识发现  2019, Vol. 3 Issue (6): 12-20    DOI: 10.11925/infotech.2096-3467.2018.0696
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
大众性问答社区答案质量排序方法研究*
易明(),张婷婷
华中师范大学信息管理学院 武汉 430079
Ranking Answer Quality of Popular Q&A Community
Ming Yi(),Tingting Zhang
School of Information Management, Central China Normal University, Wuhan 430079, China
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摘要 

目的】针对大众性问答社区答案质量参差不齐的现状, 对答案质量排序方法进行探讨。【方法】依据信息接受模型, 从感知价值角度构建答案质量排序初始指标体系; 采用K-Medoids聚类算法对初始指标进行离散化, 同时利用粗糙集理论对初始指标进行约简并赋予权值, 进而修正指标体系; 运用加权灰色关联分析计算答案的加权灰色关联度, 以产生排序结果。【结果】针对“知乎”6类话题下6个问题的2 297条相关数据进行实验分析, 排序靠前的答案通常采用图文结合的表达方式、答案所含信息量高, 且回答者社区参与度较高, 从而答案的质量较高。【局限】数据规模需要扩大, 对排序方法的评价还可以优化。【结论】73名“知乎”用户对原始排序与本研究排序进行满意度评价, 结果表明本文方法具有优越性。

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易明
张婷婷
关键词 大众性问答社区答案质量排序感知价值粗糙集理论加权灰色关联分析    
Abstract

[Objective] This paper proposes a new method to rank the quality of answers from a popular Q&A community in China. [Methods] First, based on the information acceptance model, we established initial quality indicators for the answer’s perceived values. Then, we discretized these indicators with the K-Medoids clustering algorithm. Third, we reduced and weighted the indictors with the help of rough set theory. Finally, we generated the formal rankings with the weighted grey correlation analysis. [Results] We evaluated the proposed method with 2 297 answers for six different types of questions from the Q&A website of “Zhihu”. We found that the answers ranked higher generally included textual message with images. These answers were also more informative than others and involved active members of the Q&A community. [Limitations] The size of our dataset needs to be expanded, and the evaluation method of the proposed model could be optimized. [Conclusions] The proposed method is an effective way to rank the quality of answers from the Q&A community.

Key wordsCommon Q&A Community    Answer Quality Ranking    Perceived Value    Rough Set Theory    Weighted Grey Correlation Analysis
收稿日期: 2018-07-02     
基金资助:*本文系国家社会科学基金项目“基于人类动力学的社交网络信息交流行为研究”(项目编号: 16BTQ076)的研究成果之一
引用本文:   
易明,张婷婷. 大众性问答社区答案质量排序方法研究*[J]. 数据分析与知识发现, 2019, 3(6): 12-20.
Ming Yi,Tingting Zhang. Ranking Answer Quality of Popular Q&A Community. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0696.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0696
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