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

Abstract: [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.