Purpose

In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.

Design/methodology/approach

In the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.

Findings

The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.

Research limitations/implications

The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.

Originality/value

A novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

中文翻译：

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社区问答中的解释性问答建议算法

目的

在社区问题解答（CQA）中，回答问题的人会假设读者已经掌握了答案中的内容。但是，有些读者不能理解所有内容。因此，需要对答案中出现的概念进行进一步的解释。此外，大量的问答（Q＆A）文档使手动检索变得困难。本文旨在缓解CQA网站的这些问题。

设计/方法/方法

本文提出了一种解释性问答文档的推荐算法。问答文档使用双项主题模型（BTM）建模（Yan等人，2013）。然后，使用增长中的神经气体（GNG）算法（Fritzke，1995）对Q＆A文档进行聚类。为了训练多个分类器，从“问答”类别中提取了三个特征。此后，构建集成分类模型以识别说明性关系。最后，推荐解释性的问答文件。

发现

GNG算法显示出良好的聚类性能。集成分类模型的性能优于其他分类器。解释性问答建议的效果和质量得分均很高。这些分数表明所提出的推荐算法的实用性和良好的性能。

研究局限/意义

从推荐解释性知识的新角度出发，该算法减轻了CQA中的信息过载。它为CQA中建议的研究提供了新的见识。而且，在实践中，CQA网站可以使用它来帮助检索Q＆A文档并促进对其内容的理解。但是，该算法适用于Q＆A文档的一般建议，该建议不考虑个人个性化特征。在以后的工作中，将对个性化推荐进行评估。

创意/价值

针对CQA提出了一种新颖的解释性Q＆A推荐算法，以减轻手工检索和Q＆A超载的负担。新颖的GNG聚类算法和集成分类模型提供了一种更准确的方法来标识解释性的问答文档。对解释性问答文件进行排名的方法可以提高建议的有效性和质量。该算法提高了检索解释性问答文档的准确性和效率。它可以帮助用户轻松掌握答案。