大众性问答社区答案质量排序方法研究

Ranking Answer Quality of Popular Q&A Community

【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。

[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.