Influencing factors of answer adoption in social Q&A communities from users' perspective: Taking Zhihu as an example

Purpose: Taking Zhihu as the object for a case study, we intend to analyze the key factors that have affected users on adopting answers in social Q&A(SQA) websites.Design/methodology/approach: With information adoption model(IAM) as the theoretical foundation and widely accepted evaluation criteria for answer quality in SQA sites as variables, we constructed a factor model that has influenced SQA community users to adopt offered answers. With the partial least squares(PLS) technique, our model was then empirically tested through a sample of 311 Zhihu users.Findings: Our results showed that answer usefulness is the most effective variable, and answer interactivity and answer entertainment both have positive and significant impacts on users’ attitude to adopt answers in an SQA community. Except for novelty, other three components of answer quality, i.e. knowledge, reliability, and solution to the problem have all significant effect on answer usefulness.Research limitations: First, due to the limited sample size, it is still questionable if our research results based on Zhihu could be applied to other SQA communities. Second, our questionnaires were mainly designed to investigate how users felt about the answers in an SQA site, but did not differentiate the content of the answer itself.Practical implications: As a three-year-old SQA platform, Zhihu has developed very quickly with its high-quality answers and public intellectual users, and has been regarded as one of the representatives of fast emerging Chinese SQA communities in recent years. Our studycould help shed light on users’ information sharing and knowledge adoption behaviors in a Chinese SQA site, such as Zhihu. Originality/value: Compared with previous studies on answer quality assessments in SQA sites and on information adoption model, to the best of our knowledge, this is one of the pioneer studies which combined answer qualities with users’ intention of adopting SQA answers. Our study on user answer adoption in Zhihu community could further develop the theory of IAM. This study showed that answer usefulness is the most important motivation of Zhihu users in the process of adopting answers.

Expert Knowledge-Based Apparel Recommendation Question and Answer System

Aiming at the lack of professional knowledge to guide apparel recommendation,an apparel recommendation method based on image design expert knowledge has been proposed.Then,apparel recommendation knowledge graphs have been created and a apparel recommendation question and answer(Q&A)system has been designed and implemented.The question templates in the apparel recommendation domain were defined,the task of recognizing the named entities of question sentences was completed by the Bi-directional encoder representations from transformer-Bi-directional long short-term memory-conditional random field(BERT-BiLSTM-CRF)model,and the question template with the highest matching degree to the user’s question was obtained by using term frequency-inverse document frequency(TF-IDF)algorithm.The corresponding cypher graph database query statement was generated to retrieve the knowledge graph for answers,and iFLYTEK’s voice application programming interface(API)was called to implement the Q&A.The experimental results have shown that the Q&A system has a high accuracy rate and application value in the field of apparel recommendations.

Learning natural ordering of tags in domain-specific Q&A sites

Tagging is a defining characteristic of Web 2.0.It allows users of social computing systems(e.g.,question and answering(Q&A)sites)to use free terms to annotate content.However,is tagging really a free action?Existing work has shown that users can develop implicit consensus about what tags best describe the content in an online community.However,there has been no work studying the regularities in how users order tags during tagging.In this paper,we focus on the natural ordering of tags in domain-specific Q&A sites.We study tag sequences of millions of questions in four Q&A sites,i.e.,CodeProject,SegmentFault,Biostars,and CareerC up.Our results show that users of these Q&A sites can develop implicit consensus about in which order they should assign tags to questions.We study the relationships between tags that can explain the emergence of natural ordering of tags.Our study opens the path to improve existing tag recommendation and Q&A site navigation by leveraging the natural ordering of tags.

社交问答网站知识共享的内容及社会网络分析——以知乎社区“在线教育”话题为例

为了理解社交问答网站中的内容和交互关系对知识共享水平的影响,选取知乎社区"在线教育"话题为研究对象,采用内容分析法和社会网络分析法对数据进行编码处理。内容分析发现答案大多是针对问题进行解释和辨别,较少有评估、反思和共识,评论中则多了增进感情的社交性问候和支持鼓励。社会网络分析从网络基本属性、凝聚子群和中心性三个角度出发,发现成员之间联系松散、交互较少,形成以内部交流为主的小团体,知识的分享和讨论集中在少数核心成员身上。建议从问答质量、互动程度、小团体之间的联系和精英人士参与四个方面提高社交问答网站的知识共享水平。

多媒体大学化学远程教学的研究

基于 Internet的大学化学教学网站 ,采用流行且适用范围广泛的基础平台、运行环境和技术配置 ,集教学和管理于一体 ,对网上答疑、网络考试和网上实验选修等重要模块的功能实现智能化控制 ,使先进的网络技术和远程教育成为大学课堂教学的重要补充和完善手段 ,在全国高校具有创新意义和推广价值。

基于SSM框架的网上题目录入答题系统设计

文章的主要内容是阐述搭建一个名为"DTest"的网上题目录入答题网站。它是一个通过目前比较流行的开源技术框架Spring,Spring MVC,Mybatis等搭建开发的一个网上答题网站平台。本系统开发技术主要分为3部分:后端由Java语言使用Spring,Spring MVC,MyBatis等框架进行开发;前端页面以HTML5作为信息结构,CSS3作为表示样式,JS及JQ完成业务进行开发;最后,数据持久层方面使用MySQL作为数据库开发。

Discovering semantically related technical terms and web resources in Q&A discussions

A sheer number of techniques and web resources are available for software engineering practice and this number continues to grow.Discovering semantically similar or related technical terms and web resources offers the opportunity to design appealing services to facilitate information retrieval and information discovery.In this study,we extract technical terms and web resources from a community of question and answer(Q&A)discussions and propose an approach based on a neural language model to learn the semantic representations of technical terms and web resources in a joint low-dimensional vector space.Our approach maps technical terms and web resources to a semantic vector space based only on the surrounding technical terms and web resources of a technical term(or web resource)in a discussion thread,without the need for mining the text content of the discussion.We apply our approach to Stack Overflow data dump of March 2018.Through both quantitative and qualitative analyses in the clustering,search,and semantic reasoning tasks,we show that the learnt technical-term and web-resource vector representations can capture the semantic relatedness of technical terms and web resources,and they can be exploited to support various search and semantic reasoning tasks,by means of simple K-nearest neighbor search and simple algebraic operations on the learnt vector representations in the embedding space.

学术社交网络问答质量智能评价与服务优化研究

[目的/意义]学术社交网络所提供的问答服务已成为学者们快速获取学术信息、解决学术问题的重要途径,实现基于机器学习的问答质量智能评价和服务优化对学术社交网络中优质内容传播具有重要意义。[方法/过程]以ResearchGate问答服务为研究对象,从结构化特征、内容特征、其他特征以及回答者特征4个维度构建答案质量评价体系,利用机器学习方法和数据增强技术进行答案质量分类预测。[结果/结论]SMOTE算法在处理不平衡样本时具备有效性;支持向量机在单一模型预测中,取得出色的分类效果;组合模型使预测精度得到进一步提升,基于随机森林、支持向量机、BP神经网络构建的组合模型分类性能最佳,以此为基础可通过搭建问答质量智能评价系统实现学术社交网络问答服务优化。

结构环境,初手经典——梁启超墓园体验释读

通过实地体验后对梁思成1929年设计的梁启超墓园的简朴、现代、动人特征的3个发问和测绘、研究、思考及若干谜题试解,笔者获得关于这一超越时代和同侪的作品设计特征与梁氏早期关于中国建筑特征之思想表述的密切关联性结论,即以环境空间利用配合实物结构技术,营造动人胜景情境;其中关键之处源于梁氏父子深情与对乃父人品、学问、情怀的深刻理解及表达,并依托和作用于体验者的实地亲身感受和情感共鸣,最终成就出这一经典杰作。