学术社交网络问答质量智能评价与服务优化研究

Intelligent Quality Evaluation and Service Optimization of Q&A in Academic Social Networking Site

[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。

[Purpose/significance]The Q&A service provided by academic social networking site has become an important way for scholars to access academic information quickly and solve academic problems.It is of great significance for the dissemination of high-quality content in academic social networking site to implement the intelligent evaluation of Q&A quality and the service optimization based on machine learning.[Method/process]This paper took ResearchGate as the research object,constructed an answer quality evaluation system based on four dimensions of structural features,content features,respondent characteristics and other characteristics of answers,and then used machine learning methods and data augmentation technology to perform the automatic answer quality classification prediction.[Result/conclusion]The results show that SMOTE algorithm is effective in dealing with unbalanced samples;In the first mock exam,support vector machine(SVM)achieves excellent classification performance;The combined model can further improve the prediction accuracy,and the combined model based on random forest,SVM and BP neural network has the best classification performance.On this basis,the academic social network Q&A service can be optimized by building the intelligent quality evaluation system.